6/2/21 6/2/21

Analytics Interview Questions You Want To Ask Your Future Employer

If you're looking to get a new job in data science, you may want to evaluate the "data driven-ness" of the culture when interviewing. You'll save yourself the potential heartache of watching poorly designed hypothesis tests go out the door after you've informed stakeholders of the pitfalls. We often see tons of articles that discuss what hiring managers are looking in candidates, and candidates are studying and preparing for these questions to put their best foot forward. Interviewing is also a two-way street, there's a ton of information you need to collect for yourself as well.

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Let's make sure that candidates are asking the right questions about the state of a company's data, so that they can make a truly informed decision about where they want to work. No one likes surprises. It's not that any of these questions would be a deal breaker necessarily, just make sure you know what you're getting into before you accept an offer.

There are many other questions I'd be looking to ask as well to assess the company culture. As my career has progressed, cultural fit has very much been the deciding factor when considering roles. It is absolutely important to work somewhere that you feel you can be yourself. After all, you spend a hell of a lot of time there. This article is not focused on assessing company culture, but is specifically focused on the questions you might want to ask to assess where a particular company is with their data transformation. By 'data transformation' I'm not talking about taking the natural log of one of your variables. I'm talking about the journey a company goes through while striving to modernize their approach to leveraging their data. Companies often start on a journey to become more data driven, and that's great! But it's also possible that you don't want to take a new job and find that all of their data lives in Excel spreadsheets rather than in a database (this exists folks).

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Questions:

Ask: About the relationship with stakeholders. Who has the decision rights on final test design or analysis? Do stakeholders often make decisions that are counter to analysis?

I ask about who has decision right on analytics initiatives. Are analytics and their stakeholders thought partners who collaborate effectively? Or can raising a ticket for analytics feel like asking for food from a short-order cook?Would you like t-tests with that? With work, this relationship dynamic can obviously be changed. There's also the case where a position wouldn't have as much communication with stakeholders as typical data analyst and data scientist roles, there is always room for variability.

It is certainly worth clarifying in the interview whether you can expect your thoughts on methodology to be valued on day 1, or if there is significant work to be done to achieve that. It's probably important you understand the dynamics of this relationship before taking a role.

I also want to note, it's perfectly acceptable for an organization to make strategic decisions. These would be decisions counter to any analysis performed and you decide to do them anyways. Though I do believe that if that is the case your analytics resources should be focused on higher value initiatives and not on items where the final decision has already been made.

I Ask: About the state of self service tools for stakeholders. Are dashboards currently in place? Is there a lot of ad-hoc one off analysis (or are you working on high-value big impact projects)?

Do you love building dashboards? I enjoy making a nice dashboard occasionally, but if the stakeholders don't already have self-service data access, there's a high likelihood that this is what you'd be doing, and it's nice to know this in advance. Although I actually do enjoy building dashboards, the concern I have (and have experienced first hand) is when stakeholders cannot access the level of data they need, these asks now become "ad hoc analysis". These questions can be fun, it feels good to help someone find an answer, but they're typically not higher value questions. The time you help Mary from Marketing look at the price distribution of the cheapest items in the product catalog is not going to earn a spot on your resume. All positions will have ad-hoc queries, but you might want to make sure that ad hoc requests are a piece of the gig and not the majority of the gig. Ensuring that your future stakeholders can already access the data they need to perform their job will reduce the likelihood of constant ad-hoc requests.

Ask: About data governance. Do teams across the organization all have the same understood definition of the same fields or metrics? Or will you get a different answer for certain metrics depending on which department you ask?

Ever complete an analysis only to spend the next week digging into why your number is different from Jimmy's? It's not fun. When there's no governance in place, trying to get your numbers to foot with someone else can also be common. Being a data steward for tables leveraged by analytics was probably not the most exciting part of my job, but I'd take that anytime if the alternative was lacking data governance. When there are too many questions around how things are calculated, it's easier for people to change their mind about how things are calculated (to support their interests). My wish for you is a job where the data is correct and metrics are well defined.

Ask: Are they able to understand (or have data easily accessible) that would allow you to get at the full journey of the customer from first touch points all the way through attrition??

If you don't understand the customer journey, it's possible you're performing analysis on a disjointed view rather than the whole picture. Once you have the full picture of the customer journey, your previous beliefs, stories, and analysis might not hold up. Luckily, performing this analysis would lead to a ton of juicy insights and could be a ton of fun. It's also possible, that the organization you're interviewing with doesn't have the data to make this analysis possible. For instance, I've worked with companies that did not have website click data available. In many scenarios, it's hard to build predictive models without this, so much of what we care about in e-commerce is often tied to this behavior. Asking about the customer journey is my way of starting a dialogue about what data might be missing or hard to access. There's often a number of data sources that need to be integrated to go all the way from acquisition (and the channel that they came in on) all the way to churn. There's sales data, website click data, purchase data, customer service data, lots of data. This is all about having the relevant information you need to make an informed decision.

summary:

You can obviously still join an organization that has less than stellar answers to these questions. No one (or company) is perfect! My hope is you'll at least have a real good idea of what your job might look like and the challenges you might face if you choose to work for that company. The questions here are often the same problems we read about in blogs currently. Many data companies are still working their way through these! Once you take a job, remember to keep asking questions! I've written another post about asking great questions as a data scientist, you can check it out here.

Would love to hear your thoughts on questions that help you assess the current state of data at an organization.

5/25/21 5/25/21

Business Science’s Time Series Course is Incredible

I’m a time series fan. Big fan. My first job out of grad school was for a utility company building econometric time series analysis and forecasting models. Lots of ARIMAs and neural nets. However, that was now over 10 years ago (don’t know how the hell that happened).

This post contains affiliate links that help to offset the cost of running the blog, plus the link gives you a special 15% discount. If you use the link, thank you!

In almost every position I've held in data, a question has come up that involved a time series (not a surprise that business cares about what has happened over time). Often, I was the only one who had any knowledge of time series on my team. I'm not sure why it isn't taught as a standard part of most university programs that are training data scientists, but it's just unfortunately not. I believe that understanding time series analysis is currently a great way to differentiate yourself, since many in the field are just not well versed in it.

I wanted to understand what was current in the world of applying time series analysis to business. It had been a real long time since I had given the subject some of the love and attention, and I thought taking this Business Science course would be the perfect way to do that.

My History With Business Science Courses:

I’ve previously written about Business Science’s first course, you can check it out here. I've also taken his first Shiny app course (there’s a more advanced one as well) and went from zero to Shiny app in 2 days using survey data I collected with Kate Strachnyi. It was a real win.

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The app is still on my site here, just scroll down. For this little flexdashboard app I went from basically zero Shiny to having something that was useful in 2 days leveraging only the first 25% of the course. The course cannot actually be completed in 2 days. It's also worth noting that the course builds an app with much more functionality than mine. It’s a long course.

Back to the Time Series Review:

It’s broken into three different section:

Things I freakin’ love
The sexy
Everything else

Things I freakin’ love:

You’re learning about packages from the package creator. Who is going to understand a library better than the person who wrote it?. Matt built both modeltime and timetk that are used in this course. I find that super impressive. These packages are also a step up from what was currently out there from a "not needing a million packages to do what I want" perspective.

He uses his own (anonymized) data fromBusiness Science to demonstrate some of the models. I haven’t seen others do this, and I think it’s cool. It’s a real, practical dataset of his Google Analytics and Mailchimp email data with an explanation of the fields. If you don’t have analytics experience in e-commerce and are thinking about taking a role in e-commerce, definitely give some thought to this course.

I love how in-depth he gets with the subject. If you follow all that is covered in the course, you should be able to apply time series to your own data.

The Sexy:

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Ok, so I’m sure some are interested in seeing just how “cutting edge” the course gets.

Once you're combining deep learning Gluon models and machine learning models using ensembling methods, you might be the coolest kid at work (but I’m not making any promises). Gluon is a package that was created by Amazon in Python. So you’ll leverage both Python and R for Gluon.

Some of the deep learning algorithms you’ll learn how to leverage are:

DeepAR
DeepVAR
N-Beats
Deep Factor Estimator

Module 18 of the course is where you'll get into deep learning. A couple years ago I might have said "deep learning, bah humbug, requires too much computing power and isn't necessary, simpler is better." As things change and progress (and computers get even more beefy) I'm definitely changing my tune. Especially as an ensemble N-Beats algorithm beat the ES-RNN's score in the M4 competition. M competitions are prestigious forecasting challenges, and they've historically been won by statistical algorithms. (I wouldn't have known this information without this course). The stuff being taught in this course is very current and the sexy new techniques that are winning the big competitions.

Here's a look at the syllabus for preparing the data and learning about the DeepAR model. You're doing log transformations, Fourier Series, and when you get to modeling the course even covers how to handle errors. I just love it. I know I'll be referring back to the course when a time series use case pops up in the future.

The course covers 17 different algorithms. I'm trying to think if I could name 17 algorithms off the top of my head… it’d take me a minute. ARIMA is obviously included, because It’s like the linear regression of time series. You’ll go through ARIMA, TBATS (a fave because you don’t need to worry about stationarity the way you do with ARIMA. I’ve used this one in industry as well).

Along with these other algos:

ARIMA Boost
Prophet Boost
Cubist
KNN
MARS
Seasonal decomposition models

Then you’ve got your ensemble algos being leveraged for time series:

GLMNET
Random Forest
Neural Net
Cubist
SVM

Strap in for 8 solid hours of modeling, hyperparameter tuning, visualizing output, cross-validation and stacking!

Everything else:

Matt (the owner of Business Science) speaks clearly and is easy to understand. Occasionally I'll put him on 1.25x speed.
His courses in general spend a good amount of time setting the stage for the course. Once you start coding, you’ll have a great understanding of where you’re going, goals, and context (and your file management will be top notch), but if you’re itching to put your fingers on the keyboard immediately, you’ll need to calm the ants in your pants. It is a thorough start.
You have to already feel comfy in R AND the tidyverse. Otherwise you’ll need to get up to speed first and Business Science has a group of courses to help you do that. You can see what's included here.

Before we finish off this article, one super unique part of the course I enjoyed was where Matt compared the top 4 time series Kaggle competitions and dissected what went into each of the winning models. I found the whole breakdown fascinating, and thought it added wonderful beginning context for the course.

In the 2014 Walmart Challenge, taking into account the “special event” of a shift in holiday sales was what landed 1st place. So you're actually seeing practical use cases for many of the topics taught in the course and this certainly helps with retention of the material.

Likewise, special events got me good in 2011. I was modeling and forecasting gas and the actual consumption of gas and number of customers was going through the roof! Eventually we realized it was that the price of oil had gotten so high that people were converting to gas, but that one tripped me up for a couple months. Thinking about current events is so important in time series analysis and we'll see it time and again. I've said it before, but Business Science courses are just so practical.

Summary:

If you do take this course, you’ll be prepared to implement time series analysis to time series that you encounter in the real world. I've always found time series analysis useful at different points in my career, even when the job description did not explicitly call for knowledge of time series.

As you saw from the prerequisites, you need to already know R for this course. Luckily, Business Science has created a bundle at a discounted price so that you can both learn R, a whole lot of machine learning, and then dive into time series. Plus you’ll get an additional 15% off the already discounted price with this link. If you're already comfortable in R and you're just looking to take the time series course, you can get 15% off of the single course here.

Edit: People have asked for a coupon to buy all 5 courses at once. That's something I'm able to do! Learn R, machine learning, beginner and advanced Shiny app development and time series here.

12/30/19 12/30/19

Hands-on SQL Practice For A Data Science Interview

I bet you're searching the internet for a fantastic reference to help you get ready for your SQL interview. You've landed in the right place.

Let's jump right in and get started. I have a SQL browser available for you to use here: Show me the data

The data is fictitious, but extensive and useful for writing queries.

You can use this browser to answer all of the questions below. When you're done, if you weren't able to answer a couple of the questions, you can give me your email address in the email form near the bottom of this article and I'll send you the query solutions.

This article is designed to help you with "white boarding" SQL questions. We're not going to cover any theory here. Just a bunch of questions and how to answer them leveraging SQL.

I wish you a ton of luck on your interview, I hope it results in an offer! If you're looking to further your SQL skills for data science, I have also created the ultimate course in SQL for data science. We cover tons of material that you won't see here, because feature engineering, handling NULLs, working with datetimes, etc., is not typically part of the interview. But if you want to really hit the ground running at your new job, I'd highly suggest this course. It's free and you can find it here

SQL Questions using just the select statement:

Write a query to determine the number of rows in the customer table. Answer: 351,962
What was the maximum commission paid in the customer table? The median? Answer: Max -$10,295, Average -$66.30
Write a query to that returns the customer_id, business_type and Country from the customer table.

SQL Questions using a where statement:

How many customers do we have "has_instagram" information for in the customer table? i.e. - How many rows are not NULL? Answer: 128,449
How many customers have a "First_conversion_date" greater than 1/1/2016 in the customer table? Answer: 54,397

SQL Question using a group by statement:

How many customers have "has_facebook" =1 in the customer table? Use a group by statement. Answer: 60,894

SQL Question using a group by and order statement:

Which state has the most customers? How many customers live in that state in the customer table? Answer: California, 43,736 customers

SQL Question using a having statement and subquery:

Using the billedservices table, how many customers had more than 1 billed service? Use a subquery to answer this question. Answer: 44

SQL Question requiring a join:

How many customers from OUTSIDE the United States have an entry in the billed services table? Answer: 89

In an interview, they'll typically place two or three pieces of paper up on the whiteboard. This will have your data. Obviously, this means that the data you'll be working with is much smaller.

Take home tests are typically much more difficult than what was covered here. During an in-person interview, they'll typically only have 30 minutes to an hour to assess your SQL knowledge. Most often, they just want to know that if you have SQL listed on your resume, that you can write some simple queries like up above.

I've never personally been asked to whiteboard the solution for creating a table, updating a column, etc., but obviously any SQL questions are fair game.

If you had no difficulty answering these questions, you're likely to do fine on your SQL interview.

Want to further your SQL skills for data science? Check out the Ultimate SQL for Data Science course.

Looking for the solutions to the questions above? I'll send them directly to your inbox :)

3/15/19 3/15/19

Key Ingredients to Being Data Driven

Companies love to exclaim "we're data driven". There are obvious benefits to being a data driven organization, and everyone nowadays has more data than they can shake a stick at. But what exactly does an organization need to be "data driven"?

Just because you have a ton of data, and you've hired people to analyze it or build models, does that make you data driven? No. That's not enough.

Although we think a lot about data and how to use it. Being data driven needs to be a priority at the executive level and become part of the culture of the organization; more so than simply having a team with the necessary capabilities.

Here are the baseline qualities that I believe are necessary to be effective in your "data driven-ness". Now I'm making up words.

To be data driven:

Test design and analysis is owned by analytics/data science teams.
Dashboards are already in place that give stakeholders self-serve access to key metrics. (Otherwise you'll have low value ad-hoc requests to pull these metrics, and it'll be a time sink.)
Analytics/Data Science teams collaborate with the business to understand the problem and devise an appropriate methodology.
Data governance and consistent usage of data definitions across departments/the organization.
You have a data strategy.

You'll notice that there is a lack of fancy hype buzzwords above. You don't need to be "leveraging AI" or calling things AI that are in fact hypothesis tests, business logic, or simple regression.

I don’t believe fancy models are required to consider yourself data driven. A number of the points listed above are references to the attitudes of the organization and how they partner and collaborate with analytics and data science teams . I love building models as much as the next data scientist, but you can't build next level intelligence on a non-existent foundation.

To clarify, I'm not saying every decision in the organization needs to be driven by data to be data driven. In particular, if you're going to make a strategic decision regardless of the results of a test or analysis, then you should skip doing that test. I'm a big advocate of only allocating the resources to a project if you're actually going to USE the results to inform the decision.

Let's take a look at the points from above.

Test design and analysis is owned by analytics/data science teams:

Although data science and analytics teams often come up with fantastic ideas for testing. There are also many ideas that come out of a department that is not in analytics. For instance, in eCommerce the marketing team will have many ideas for new offers. The site team may want to test a change to the UI. This sometimes gets communicated to the data teams as "we'd like to test "this thing, this way". And although these non analytics teams have tremendous skill in marketing and site design, and understand the power of an A/B test; they often do not understand the different trade-offs between effect size, sample size, solid test design, etc.

I've been in the situation more than once at more than one company where I'm told "we understand your concerns, but we're going to do it our way anyways." And this is their call to make, since in these instances those departments have technically "owned" test design. However, the data resulting from these tests is often not able to be analyzed. So although we did it their way, the ending result did not answer any questions. Time was wasted.

Dashboarding is in place:

This is a true foundational step. So much time is wasted if you have analysts pulling the same numbers every month manually, or on an ad-hoc basis. This information can be automated, stakeholders can be given a tour of the dashboards, and then you won't be receiving questions like "what does attrition look like month over month by acquisition channel?" It's in the dashboard and stakeholders can look at it themselves. The time saved can be allocated to diving deep into much more interesting and though provoking questions rather than pulling simple KPIs.

Analytics/Data Science teams collaborate with the business on defining the problems:

This relationship takes work, because it is a relationship. Senior leaders need to make it clear that a data-driven approach is a priority for this to work. In addition, analytics often needs to invite themselves to meetings that they weren't originally invited to. Analytics needs to be asking the right questions and guiding analysis in the right direction to earn this seat at the table. No relationship builds over night, but this is a win-win for everyone. Nothing is more frustrating than pulling data when you're not sure what problem the business is trying to solve. It's Pandoras Box. You pull the data they asked for, it doesn't answer the question, so the business asks you to pull them more data. Stop. Sit down, discuss the problem, and let the business know that you're here to help.

Data governance and consistent usage of data definitions across departments/the organization:

This one may require a huge overhaul of how things are currently being calculated. The channel team, the product team, the site team, other teams, they may all be calculating things differently if the business hasn't communicated an accepted definition. These definitions aren't necessarily determined by analytics themselves, they're agreed upon. For an established business that has done a lot of growing but not as much governance can feel the pain of trying to wrangle everyone into using consistent definitions. But if two people try to do the same analysis and come up with different numbers you've got problems. This is again a foundation that is required for you to be able to move forward and work on cooler higher-value projects, but can't if you're spending your time reconciling numbers between teams.

You have a data strategy:

This data strategy is going to be driven by the business strategy. The strategy is going to have goals and be measurable. The analyses you plan for has a strong use case. People don't just come out of the woodwork asking for analysis that doesn't align to the larger priorities of the business. Things like "do we optimize our ad spend or try to tackle our retention problem first?" comes down to expected dollars for the business. Analytics doesn't get side-tracked answering lower value questions when they should be working on the problems that will save the business the most money.

In Summary:

I hope you found this article helpful. Being data driven will obviously help you to make better use of your data. However, becoming data driven involves putting processes into place and having agreement about who owns what at the executive level. It's worth it, but it doesn't happen over night. If you're not yet data driven, I wish you luck on your journey to get there. Your analysts and data scientists will thank you.

If you have suggestions on what else is required to be data driven, please let me know your thoughts!

12/20/18 12/20/18

Data Moved Me in 2018

Dear diary,

I'm writing this article so that a year from now when I've completely forgotten how cool 2018 was, I can look back on this post. I'm literally floored by all that transpired this year, here is a small snapshot in chronological-ish order:

I started a new position in January 2018 as a Senior Data Scientist at Constant Contact. I've been fortunate to work on interesting projects throughout the year that have often served as inspiration for blog posts.

I launched my first blog article (ever) in March of 2018. This was originally on the domain kristenkehrer.com which is no longer live. This first blog article was rejected by Towards Data Science on Medium. My 2nd blog article was accepted, and now I cross-post most of my articles on TDS. (I've said this before, but if you're blogging and you get rejected, just keep coming back ;)

I spoke on a panel at Hult International Business School on how to get into data science.

I launched datamovesme.com in July after banging my head against the wall trying to figure out Wordpress. I made this move because I knew I'd like to eventually launch a course on my own hosted site and the website builder I was using for kristenkehrer.com would not allow me to do that. In addition, my previous website was never going to rank for SEO.

I spoke with Mike Delgado at Experian on the DataTalk Podcast. So many laughs, fun, and data science in this episode, give it a listen :)

In the end of August I launched my first ever online course "Up-Level Your Data Science Resume." It has helped so many people effectively market themselves and land jobs in data science positions. When people email me to tell me that they have found a job it literally brightens my week.

I was invited to join the YouTube channel Data Science Office Hours with Sarah Nooravi, Eric Weber, Tarry Singh, Kate Strachnyi, Favio Vazquez, Andreas Kretz and newly added Matt Dancho. It's given me the opportunity to create friendships with these wonderful and intelligent people who are all giving back to the community. I want to give a special shout out to Mohamed Mokhtar for creating wonderful posters for office hours. You can check out previous episodes on the Data Science Office Hours YouTube channel (link above).

August 22nd was Favio Vazquez and I launched Data Science Live. We've had incredible guests, take questions from the community, and generally just talk about important topics in data science in industry. We already have some amazing guests planned for 2019 that I cannot wait to hear their perspective and learn from them.

I spoke at Data Science Go in October and had the time of my life. It was basically the king of data parties. I'm grateful to Kirill Eremenko and his team for giving me the opportunity. My talk was around how to effectively communicate complex model output to stakeholders. I went through 4 case studies and demonstrated how I've evolved through time to position myself as a though partner with stakeholders. I also had the opportunity to speak on a panel discussing women in data and diversity. I love sharing my experience as a woman in data and also how I'm able to be an ally and advocate for those who aren't always heard at work.

I was also on the SuperDataScience Podcast in November. Getting to chat 1-on-1 with Kirill was fantastic. He has great energy and was a joy to speak with.

In November I was #8 LinkedIn Top Voices 2018 in Data Science and Analytics. That still seems a little surreal. Then in December LinkedIn sent me a gift after I wrote an article about the wonderful data science community on LinkedIn. That's also pretty nuts.

I picked up a part-time job as a Teaching Assistant for an Applied Data Science online course through Emeritus. Being at DSGO made me think of how I'm contributing to the community, and having the opportunity to help students learn data science has given me extra purpose while helping to keep my skills sharp. It's really a win all around.

It's been a jam-packed year and at times a little hectic between the 9-5, my two young children, and all the fun data science related activities I've participated in. Luckily I have a husband who is so supportive; all of these extracurricular activities wouldn't be possible without him.

Looking to 2019:

I've set some big goals for myself and already have a number of conferences I'll be speaking at in the calendar. I can't wait to share some of these exciting new ventures in the New Year. I wish you a wonderful holiday and can't wait to see and engage with you in 2019.

8/24/18 8/24/18

Strong Data Science Content for Your Resume

The biggest pain point or challenge I hear when people are writing their resume is that they want concise, crisp, effective content that sounds impactful. But they’re not sure how to write that wonderful content they want.

There is so much to consider when thinking about your content. There are many different traits you want to showcase on your resume that the business values for any given position. There is much more to a successful data science hire than just technical and machine learning ability, and we'll want to think about how to best position these skills as well. Here are some quick examples of how you can up-level the content on your resume to get you started.

Here we’re going to cover:

Starting with a verb
Ending with the value you provided

Starting with a verb

Strong statements start with an action verb. A short list of some verbs that you can try to apply to your experience include:

Built
Delivered
Developed
Increased efficiency
Created
Evaluated
Trained

Try to vary your verbs as well. Don’t use the same one over and over again throughout your resume.

So we have some words, let’s look at some real examples from resumes and how the statements improve by starting with a verb.

This first example comes from a math teacher who is learning data science through MOOCs and is planning to make a career change.

Original: “I ran live lessons on Blackboard Collaborate and attended meetings via the computer.”

Updated: "Presented math training virtually, delivered mathematical concepts in a way that students could easily comprehend and learn."

This shows that she is able to break down material and communicate well. The following would also work:

Updated (another version): "Conducted virtual meetings with expert communication. Provided students the ability to receive one-on-one guidance to keep them on pace in a way that fit their schedule."

The next example is from a BI professional who is also looking to make a move to data science:

Original: “Participation in Global Transformation Program as Commercial Finance Business Intelligence (BI) expert (Credit and Collections), in the definition of KPIs and Global template Reports. Testing, Business Readiness and Post Go live support for Ecuador implementation (Releases 1, 2 and 3). Support to front office area (sales and distribution).”

Here, our example owned the definition of KPIs and reporting. She also contributed cross-functionally to help make this project a success. Talking about ownership of KPIs, and being a strong contributor cross-functionally sounds stronger when we begin with a verb instead of “participation” (noun).

Updated: "Owned definition of KPIs and reporting, ensuring accuracy and allowing for self-service of key metrics by stakeholders."I'd certainly need to create more bullet points to capture all of the information in the original, but this is an idea of what we're trying to achieve.

Ending your statements with the result or value

Let’s look at an opportunity for improvement that was on my resume for a while.

Original: “Built Neural Network models to forecast hourly electric load.”

Cool story, but did I just build it for fun? Or was it useful? Especially in a space where businesses are all too familiar with someone building a fancy model, and then it never gets used for anything, it is of utmost importance that you clearly demonstrate how your work was utilized.

Spell. it. out.

Updated: “Built Neural Network models to forecast hourly electric load. Model output was imperative during extreme weather and was used for capacity planning decisions.”

Now I have a statement that shows not only that I delivered a model, but that model delivered value to the business.

Maybe your previous work experience doesn’t involve building a model. Maybe you built a dashboard. Did that dashboard allow your stakeholders to get valuable information on their own (referred to as self-service)? That’s value. Did the dashboard reduce the amount of time spent on ad-hoc, low value data aggregation so you could focus on higher value initiatives? That’s value, because here you’re increasing efficiency.

Using verbs as your starting point and demonstrating the value your work provided is a great step towards marketing yourself and showcasing your talents. Think deeply about what was the purpose of the work, and spell that out on your resume.

8/22/18 8/22/18

Trying to Change Careers or Get Your Start in Data Science?

If you’re someone who is looking to make a move to data science, there are some ways that you can polish your approach to get noticed during your job search.

Assuming that you've built up the skills required for the job see if you're able to leverage some of these tips:

Optimize your resume (as best you can) for the job you WANT not the jobs you’ve HAD.
Try to gain experience at your current job (if you’re a career changer), or work on your own data science projects at home. (continuous learning is a big plus).
Develop a killer elevator pitch.

Optimizing your resume for the job you want:

Describe your projects in a way that shows you’re results-focused.

The points you’re going to want to demonstrate on your resume need to both:

Demonstrate that you understand general corporate culture, and showcase your collaborative, result achieving, problem solving and self-managing competencies.
Show that you have the technical chops as a data scientist.

The first bullet takes a lot of thought - it is really easy to list job duties, it’s another thing to reword them effectively to highlight your true strengths and demonstrate how what you've done has improved the business. Your bullet points should be full of action verbs and results, even if you need to stretch yourself mentally to identify these.

Did you automate a process that saved hours of time manually doing a task? That time saved is business value.
Demonstrating that you've worked cross-functionally or presented results to the business are again, things that are desirable for the new job you want (data scientist).

It is helpful to read job descriptions and see what companies are looking for, you'll find consistent themes. If you look closely, you'll see there are a lot of skills listed that aren't necessarily technical. Make sure you shine when speaking to those softer skills. But of course, these softer skills need to be demonstrated in a way that still demonstrates an action and result. Do not just put a "soft skills" section on your resume and list a bunch of words with no context.

"Show you have the technical chops as a data scientist". This is pretty straight-forward. Try to use the verbiage from the actual job description for the job you're applying to. You might want to sound fancy, but “empirical bayesian 3-stage hierarchical model” probably isn’t on the job description. Having this specifically listed on your resume isn’t going to help you pass ATS (the applicant tracking system), and the person in human resources who doesn’t have a data science background is not going to know whether that is relevant or not. Again, looking at multiple job descriptions and trying to gauge what type of language to use on your resume is helpful.

Gain experience at your current job or work on a project:

If you currently have a job, do you have access to SQL? Does your company have a data warehouse or database? Can you file a ticket with the service desk to get SQL? Can you then play with data to make your own project?

You could even go a step further and bring data from the database into R or Python. Maybe you make a nice decision tree that answers a business questions then wonderfully and concisely place your results of your project on your resume.

Try to automate a task that’s repeatable that you do on a regular cadence. That’s next level resume content. You’re increasing efficiency in this scenario.

If you’ve done data science projects on your own to round out your resume, make sure those bullets are full of action verbs and results, action verbs and results. I almost want to say it a third time.

SQL Lite is open source, R is open source, Python is open source, there is tons of free data out there. The world can really be your oyster, but you’ll need to market these go-getter skills effectively.

Develop a killer elevator pitch:

A strong, well-targeted resume might open the door, but you need to keep that door open and keep the conversation going once the door has been opened. The resume does nothing more than open the door, that’s it.

Getting your resume into the right hands can sometimes be difficult. Leveraging LinkedIn effectively can help bridge that gap. How do we begin the conversation if you’re reaching out to someone on LinkedIn to ask about opportunities?

Important note: When cold reaching out to people on LinkedIn, this should be after you have visited the company website, found a job that you’re interested in and (pretty much) qualified for, and then you reach out to a relevant person with a well-targeted message.

It is impossible to be well-targeted if you are reaching out to someone who works at a company that doesn’t have any positions available. Because you didn’t read a job description. So you wouldn’t be able to infer the needs of the business. Data Science is a large field, with many specializations, a blanket approach will not work.

Back to the pitch. You’re results-focused, you’re innovative, and you view things from the business’ perspective.

I'd suggest starting with something conversational, this will help if the person you're messaging is already being inundated with requests. A comment about a post they made recently makes your connection come across as more authentic.
Why you’re messaging: you’re interested in the open position, and you’re trying to get your resume to the correct person.
Then mention a number of things concisely that are specifically mentioned on the job description. Basically saying “hi, look at me, I’m a fit.”
Let them know that you’d really appreciate it if they’d simply forward you to the correct person (hopefully the person you’re messaging is the correct person, but there is also a chance it’s not the right person, so don’t assume).
Close strong. You’re here to add value for the company, not to talk about your needs; imply you’re aware that you’re here to talk about how you can fit the needs of the business.

Hi [name],

I enjoyed your recent post on [topic] and I look forward to reading more of your posts.

I noticed [company] is hiring for [position title], and I’m hoping I can get my resume in the right hands. I have an MS in Statistics, plus 7 years of real-world experience building models. I’m a wiz at SQL, modeling in R, and I have exposure to Python.

I’d appreciate the opportunity to speak with the appropriate person about the open position, and share how I’ve delivered insights and added value for companies through the use of statistical methods.

Thanks, Kristen

Now you may have a very different background from me. However, you can talk about the education that you do have (concisely), the exposure that you do have to building models, about your technical chops, and that you want to deliver value.

I hope that you’ll be able to use some of these suggestions. And I wish you a successful a rewarding career in data science. If you have additional suggestions for trying to make a change to data science, I’d love to hear your thoughts! The next article I post will be covering how to write crisp content for your resume that makes an impact, that article is here.

8/20/18 8/20/18

Up-Level Your Data Science Resume - Getting Past ATS

This series is going to dive into the tip of the iceberg on how to create an effective resume that gets calls. When I surveyed my email list, the top three things that people were concerned about regarding their resumes were:

Being able to get past ATS (Applicant Tracking System)
Writing strong impactful bullet points instead of listing “job duties”
How to position yourself when you haven’t had a Data Science job previously

This article is the first part of a three-part series that will cover the above mentioned topics. Today we’re going to cover getting past ATS.

If you’re not familiar with ATS, it stands for Applicant Tracking System. If you’re applying directly on a website for a position, and the company is medium to large, it’s very likely that your resume will be subject to ATS before:

1. Your resume lands in the inbox of HR

2. You receive an automated email that looks like this:

It’s hard to speak for all ATS systems, because there are many of them. Just check out the number of ATS systems that indeed.com integrates with https://www.indeed.com/hire/ats-integration.

So how do you make sure you have a good chance of getting past ATS?

1. Make it highly likely that your resume is readable by ATS

2. Make it keyword rich, since ATS is looking for keywords specific to the job

Being readable by ATS:

There has been a movement lately to create these gorgeously designed resumes. You’ll see people “Tableau-ize” their resume (ie — creating a resume using Tableau), include logos, or include charts that are subjective graphs of their level of knowledge in certain skill sets. An example of one of these charts looks like this:

ATS is not going to know what to do with those dots, just as it wouldn’t know what to do with a logo, your picture, or a table; do not use them. To test if your resume is going to be parsed well by ATS, try copying the document and pasting it in word. Is it readable? Or is there a bunch of other stuff? You can also try saving it as plain text and see what it looks like.

As data-loving story tellers, I understand the desire to want to show that you’re able to use visualizations to create an aesthetically appealing resume. And if you’re applying through your network, and not on a company website, maybe you’d consider these styles. I’m not going to assume I know your network and what they’re looking for. And of course, you can have multiple copies of your resume that you choose to use for specific situations.

What is parsable:

I’ve seen a number of blog posts in the data world saying things to the tune of “no one wants to see one of those boring old resumes.” However, those boring resumes are likely to score higher in ATS, because the information is parsable. And you can create an aesthetically pleasing, classic resume.

Some older ATS systems will only parse .doc or .docx formats, others will be able to parse .pdf, but not all elements of the .pdf will be readable if you try to use the fancy image types mentioned above.

Making your resume rich with keywords:

This comes in 2 forms:

1. Making sure that the skills mentioned in these job descriptions are specifically called out on your resume using the wording from the JD.

2. Reducing the amount of “fluff” content on your resume. If your bullets are concise, the ratio of keywords to fluff will be higher and will help you score better.

For point 1, I specifically mention my skills at the top of my resume:

I also make a point to specifically mention these programs and skills where applicable in the bullet points in my resume. If a job description calls for logistic regression, I would add logistic regression specifically to my resume. If the JD calls for just “regression,” I’ll leave this listed as regression on my resume. You get the idea.

It's also important to note that more than just technical skills matter when reading a job description. Companies are looking for employees who can also:

communicate with the business
work cross-functionally
explain results at the appropriate level for the audience that is receiving the information.

If you’re applying for a management position, you’re going to be scored on keywords that are relevant to qualities that are expected of a manager. The job description is the right place to start to see what types of qualities they’re looking for. I’ll have highlighted specific examples in my resume course I’m launching soon.

For point 2, you want to make your bullet points as concise as possible. Typically starting with a verb, mentioning the action, and the result. This will help you get that ratio of “keywords:everything” as high as possible.

In my next article in this series I'm sharing tips on how to position yourself for a job change. That article is here.

7/2/18 7/2/18

How to Ace the In-Person Data Science Interview

I’ve written previously about my recent data science job hunt, but this article is solely devoted to the in-person interview. That full-day, try to razzle-dazzle em’, cross your fingers and hope you’re well prepared for what gets thrown at you. After attending a ton of these interviews, I’ve found that they tend to follow some pretty standard schedules.

But first, if your sending out job applications and aren't hearing back, you'll want to take a second look at your resume. I've written a couple articles on how to create a strong resume. One helpful article is

here.

You may meet with 3–7 different people, and throughout the span of meeting with these different people, you’ll probably cover:

Tell me about yourself
Behavioral interview questions
“White boarding” SQL
“White boarding” code (technical interview)
Talking about items on your resume
Simple analysis interview questions
Asking questions of your own

Tell me about yourselfI’ve mentioned this before when talking about phone screens. The way I approach this never changes. People just want to hear that you can speak to who you are and what you’re doing. Mine was some variation of:I am a Data Scientist with 8 years of experience using statistical methods and analysis to solve business problems across various industries. I’m skilled in SQL, model building in R, and I’m currently learning Python.

Behavioral Questions

Almost every company I spoke with asked interview questions that should be answered in the STAR format. The most prevalent STAR questions I’ve seen in Data Science interviews are:

Tell me about a time you explained technical results to a non-technical person
Tell me about a time you improved a process
Tell me about a time with a difficult stakeholder, and how was it resolved

The goal here is to concisely and clearly explain the Situation, Task, Action and Result. My response to the “technical results” questions would go something like this:Vistaprint is a company that sells marketing materials for small businesses online (always give context, the interviewer may not be familiar with the company). I had the opportunity to do a customer behavioral segmentation using k-means. This involved creating 54 variables, standardizing the data, plenty of analysis, etc. When it was time to share my results with stakeholders, I had really taken this information up a level and built out the story. Instead of talking about the methodology, I spoke to who the customer segments were and how their behaviors were different. I also stressed that this segmentation was actionable! We could identify these customers in our database, develop campaigns to target them, and I gave examples of specific campaigns we might try. This is an example of when I explained technical results to non-technical stakeholders. (always restate the question afterwards).For me, these questions required some preparation time. I gave some real thought to my best examples from my experience, and practiced saying the answer. This time paid-off. I was asked these same questions over and over throughout my interviewing.

White Boarding:

White Boarding SQL

This is when the interviewer has you stand at the whiteboard an answer some SQL questions. In most scenarios, they’ll tape a couple pieces of paper up on the whiteboard. I have a free video course on refreshing SQL for the data science interview

here.

White Boarding Code

As mentioned in my previous article. I was asked FizzBuzz two days in a row by two different companies. A possible way to write the solution (just took a screenshot of my computer) is below:

The coding problem will most likely involve some loops, logic statements and may have you define a function. The hiring manager just wants to be sure that when you say you can code, you at least have some basic programming knowledge.

Items on Your Resume

I’ve been asked about all the methods I mention on my resume at one point or another (regression, classification, time-series analysis, MVT testing, etc). I don’t mention my thesis from my Master’s Degree on my resume, but casually referenced it when asked if I had previously had experience with Bayesian methods.

The interviewer followed up with a question on the prior distributions used in my thesis.

I had finished my thesis 9 years ago, couldn’t remember the priors and told him I’d need to follow up.

I did follow up and send him the answer to his question, they did offer me a job, but it’s not a scenario you want to find yourself in. If you are going to reference something, be able to speak to it. Even if it means refreshing your memory by looking at wikipedia ahead of the interview. Things on your resume and projects you mention should be a home run.

Simple Analysis Questions

Some basic questions will be asked to make sure that you have an understanding of how numbers work. The question may require you to draw a graph or use some algebra to get at an answer, and it’ll show that you have some business context and can explain what is going on. Questions around changes in conversion, average sale price, why is revenue down in this scenario? What model would you choose in this scenario? Typically I’m asked two or three questions of this type.

I was asked a probability question at one interview. They asked what the expected value was of rolling a fair die. I was then asked if the die was weighted in a certain way, what would the expected value of that die be. I wasn’t allowed to use a calculator.

Questions I asked:

Tell me about the behaviors of a person that you would consider a high-performing/high-potential employee.

Honestly, I used the question above to try and get at whether you needed to work 60 hours a week and work on the weekends to be someone who stood out. I pretty frequently work on the weekends because I enjoy what I do, I wouldn’t enjoy it if it was expected.

What software are you using?

Really, I like to get this question out of the way during the phone screen. I’m not personally interested in working for a SAS shop, so I’d want to know that upfront. My favorite response to this question is “you can use whatever open source tools you’d like as long as it’s appropriate for the problem.”

Is there anything else I can tell you about my skills and qualifications to let you know that I am a good fit for this job?

This is your opportunity to let them tell you if there is anything that you haven’t covered yet, or that they might be concerned about. You don’t want to leave an interview with them feeling like they didn’t get EVERYTHING they needed to make a decision on whether or not to hire you.

When can I expect to hear from you?

I also ask about the reporting structure, and I certainly ask about what type of projects I’d be working on soon after starting (if that is not already clear).

Summary

I wish you so much success in your data science interviews. Hopefully you meet a lot of great people, and have a positive experience. After each interview, remember to send your thank you notes! If you do not receive an offer, or do not accept an offer from a given company, still go on LinkedIn and send them connection requests. You never know when timing might be better in the future and your paths might cross.

To read about my job hunt from the first application until I accepted an offer,

click here

How to ace the data science in-person interview. Data analysis, data collection , data management, data tracking, data scientist, data science, big data, data design, data analytics, behavior data collection, behavior data, data recovery, data analyst. For more on data science, visit www.datamovesme.com.

7/2/18 7/2/18

The Successful Data Science Job Hunt

The point of this article is to show you what a successful Data Science job hunt looks like, from beginning to end. Strap-in, friends. I’m about to bring you from day 1 of being laid-off to the day that I accepted an offer. Seriously, it was an intense two months.I have an MS in Statistics and have been working in Advanced Analytics since 2010. If you’re new to the field, your experience may be different, but hopefully you’ll be able to leverage a good amount of this content.We’re going to cover how I leveraged LinkedIn, keeping track of all the applications, continuing to advance your skills while searching, what to do when you receive an offer, and how to negotiate.

Day 1 Being Laid-off

dyed my hair bright pink before job hunting

Vistaprint decided to decrease it’s employee headcount by $20 million dollars in employee salary, I was part of that cut. I was aware that the market was hot at the moment, so I was optimistic from day 1. I received severance, and this was an opportunity to give some real thought about what I would like my next move to be.I happened to get laid-off 4 days after I had just dyed my hair bright pink for the first time, that was a bummer.I actually went to one job interview with my pink hair, and they loved it. However, I did decide to bring my hair back to a natural color for the rest of my search.

Very First Thing I Did:

I am approached by recruiters pretty frequently on LinkedIn. I always reply.Although if you’re just getting into the field, you may not have past messages from recruiters in your LinkedIn mail, but I mention this so that you can start to do this throughout the rest of your career.Now that I was looking, my first action was to go through that list, message everyone and say:“Hi (recruiter person), I’m currently looking for a new opportunity. If there are any roles you’re looking to fill that would be a good fit, I’d be open to a chat.”

There were a number of people that replied back saying they had a role, but after speaking with them, it didn’t seem like the perfect fit for me at the moment.In addition to reaching out to the recruiters who had contacted me, I also did a google search (and a LinkedIn hunt) to find recruiters in the analytics space. I reached out to them as well to let them know I was looking. You never know who might know of something that isn’t on the job boards yet, but is coming on soon.

First Meeting With the Career Coach

As part of the layoff, Vistaprint set me up with a career coach. The information she taught me was incredibly valuable, I’ll be using her tips throughout my career. I met with Joan Blake from Transition Solutions. On our first meeting, I brought my resume and we talked about what I was looking for in my next role.Because my resume and LinkedIn had success in the past, she did not change much of the content on my resume, but we did bring my skills and experience up to the top, and put my education at the bottom.

They also formatted it to fit on one page. It’s starting to get longer, but I’m a believer in the one page resume.I also made sure to include a cover letter with my application. This gave me the opportunity to explicitly call out that my qualifications are a great match with their job description. It’s much more clear than having to read through my resume for buzzwords.I kept a spreadsheet with all of the companies I applied to. In this spreadsheet I’d put information like the company name, date that I completed the application, if I had heard back, the last update, if I had sent a thank you, the name of the hiring manager, etc.This helped me keep track of all the different things I had in flight, and if there was anything I could be doing on my side to keep the process moving.

Each Application:

For each job I applied to, I would then start a little hunt on LinkedIn. I’d look to see if anyone in my network currently worked for the company. If so, they’d probably like to know that I’m applying, because a lot of companies offer referral bonuses. I’d message the person and say something like:Hey Michelle,I’m applying for the Data Scientist position at ______________. Any chance you’d be willing to refer me?

If there is no one in my network that works for the company, I then try and find the hiring manager for the position. Odds are it was going to be a title like “Director (or VP) of Data Science and Analytics”, or some variation, you’re trying to find someone who is a decision maker.This requires LinkedIn Premium, because I’m about to send an InMail. My message to a hiring manager/decision maker would look something like:

Hi Sean,I’m interested in the remote Data Science position, and I’m hoping I can get my resume in the right hands. I have an MS in Statistics, plus 7 years of real-world experience building models. I’m a wiz at SQL, modeling in R, and I have some exposure to Python.I’d appreciate the opportunity to speak with the appropriate person about the open position, and share how I’ve delivered insights and added value for company’s through the use of statistical methods.Thanks, Kristen

Most people actually responded, Joan (the career coach) was surprised when I told her about my cold-calling LinkedIn success.

I Started Applying to Jobs, and Started Having “Phone Screens”

Phone screens are basically all the same. Some were a little more intense and longer than others, but they were all around a half hour, and they’re typically with someone in HR. Since it’s HR, you don’t want to go too deep in the technical stuff, you just want to be able to pass this stage, follow up with a note thanking them for their time, and try to firm up when you’ll be able to speak with the hiring manager :)Tell me about yourself:People just want to hear that you can speak to who you are and what you’re doing.

Mine was some variation of:

I am a Data Scientist with 7 years of experience using statistical methods and analysis to solve business problems across various industries. I’m skilled in SQL, model building in R, and I’m currently learning Python.

What are you looking to do?I’d make sure that what I’m looking to do ties directly to the job description. At the end of the day, it was some variation of:

“I’m looking to continuously learn new tools, technologies and techniques. I want to work on interesting problems that add business value”.

Then I’d talk about how interesting one of the projects on the job description sounded.What are you looking for in terms of salary?Avoid this question if you can, you’ll be asked, but try to steer in a different direction. You can always reply with “I’ve always been paid fairly in the past, I trust that I’ll be paid fairly working for [insert company name]. Do you have an idea of the salary range for the position”.They’ll know the range for the position, but they’ll probably tell you that they don’t. Most of the time I’d finally concede and give them my salary, this doesn’t mean that you won’t be able to negotiate when you receive an offer.

All The While, I’m Still Learning, And Can Speak to This in Interviews:

If I was going to tell everyone that I was very into learning technologies, I better be “walking the walk” so to speak. Although I am constantly learning, because it’s in my nature. Make sure that if you say you’re learning something new, you’re actually studying it.

The course I took was: Python for everybody

Disclaimer: This is an affiliate link, meaning that at no cost to you, I will earn a commission if you end up signing up for this course.

This course goes over your basic lists, arrays, tuples, defining a function.. but it also goes over how to access and parse web data. I had always wanted to know how to access Twitter data for future analysis, so this was super cool. The specialization (that’s the name they give for a series of courses on Coursera) also gives a brief overview in how to construct a database. This was a super bonus for me, because if I want to operationalize a model, I’m going to want to know how to write from Python to a database table. All-in-all, I found this course to be a great use of my time, and I finished it being able to speak to things intelligently, that I was not able to speak to prior to taking the course.

In Person Interviews:

I've written a whole article on in person interviews: here

At some point, you might receive a call saying they plan on putting an offer together for you, if you're still interested.Great! You’ve got an offer coming. At this point, you want to call all the other companies that you would consider an offer from and say “I’ve been informed that I am expecting an offer, is there anything you can do to accelerate your process?”I mentioned this to 2 companies. One of them did speed up their process and it resulted in an additional offer. The other company said that they would not speed up their process, I thanked them for their time and said I'd hope to cross paths in the future.

Negotiating:

The phone rings, and you answer. This is it, you’re getting your first offer. It’s time to negotiate. Only a relatively small percentage of people ever negotiate their salary, the percentage is even smaller when we’re talking about women.Ladies! Negotiate! I’m here rooting for you, you got this.Joan from Transition Solutions had coached me on this. She said “Don’t try and solve the problem for them”.When they call, let them know how excited you are that they called, and that you’re interested in hearing their offer.

Once you’ve heard the salary, vacation time, and that they’re going to send over the benefits information, you can say something along the lines of:

"Thank you so much for the offer, I really appreciate it. You know, I was hoping that you could do more on the salary."

Then wait for a response, and again be positive. They’ll most likely say that they need to bring this information back to the hiring manager."

Great! I look forward to hearing back from you. I’ll take some time to look over the benefits package. Want to speak again on ____. I’m feeling confident that we can close this."

Then you’d be walking away from the conversation with a concrete time that you’ll speak to them next, and you let them know that you were happy to hear from them, all of this is positive!I successfully negotiated my offer, and started a week later. I couldn’t be happier with where I am now and the work I’m doing. It took a lot of applying and a lot of speaking with companies who weren’t “the one”, but it was worth it.To sum up my job search. I learned that a targeted cover letter and directly applying on a company website greatly increase the response rate on your applications.

I learned that you can effectively leverage LinkedIn to find the decision maker for a position and they’ll help keep the process moving if you’re a good fit. I also gained a ton of confidence in my ability to articulate my skills, and this came with practice. I wish you lots of success on your hunt, and I hope that there was a couple of tips in this article that you are able to use :)