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.

7/2/18 7/2/18

What Getting a Job in Data Science Might Look Like

I’ve read a number of articles stating how hard it was to get into Analytics and Data Science. This hasn’t been my experience, so I wanted to share. We’ll look at interviewing, the tools I currently use, what parts of industry I wasn’t prepared for in school, and what my career trajectory has looked like. But not in that particular order.It probably makes sense to quickly recap my education before we dive in!

In 2004 — Completed a BS in Mathematics from UMASS Dartmouth
Had a 3.8 GPA in my major
Took FORTRAN while there (wasn’t good at it)
No internships
I LOVE math, and loved my time in school

Honestly, not much worth noting 2004–2007. I was “finding myself,” or something.In 2007 — Started MS in Statistics at WPI Part-Time while working for Caldwell Banker Real Estate Brokerage.

The “Housing bubble” burst (the kick-off for the Great Recession), and at the same time I was lucky to be offered a Teaching Assistantship at WPI.
Moved to Worcester and finished my MS Full-Time (Finished 2010)
Used SAS & R in classes
Still no internships (economy was bad, and I had yet to learn a ton about job searching, networking, and didn’t make use of the career center)
Thought I wanted to teach at a Community College, but two Professors asked if I’d be interested in interviewing at a local utility company (and the company happened to be 3 miles from my parents house).

I interviewed at that one company and took that job.At my first post-grad school industry job, NSTAR (now Eversource) I was a Forecast Analyst using Econometric Time-Series analysis to forecast gas and electric load (read — how much gas and electricity we need to service the customers).

Everyday I was building ARIMA models, using various statistical tests to test for structural breaks in the data, unit root tests for stationarity, and I wrote a proof to explain to the Department of Public Utilities why my choice of t-stats with a value > 1 (even though the p-value might be 0.2) were beneficial to have in the model for forecasting purposes.

I built cool Neural Nets to forecast hourly electric load. This methodology made sense because there is a non-linear relationship between electric load and the weather. The model results were fantastic, and were used to make decisions on how to meet capacity on days projected to need a high load.This is the first time that I learned that once you complete a project that people care about, you’ll most likely write a deck explaining the problem and outcomes.. and then you go “on tour”. Meaning, I created PowerPoint slides and presented my work to other teams. My first PowerPoint was not very good.

It has taken years of experience to get to a point where I now think that my decks are visually appealing, appropriately tailored for the audience I’m speaking to (have the right “level” of information), and engaging.

At NSTAR I also used a tiny bit of SAS. This was in the form of re-running code previously written by someone else. It sometimes also involved slightly modifying code that someone else had written, I definitely wouldn’t consider this job SAS intensive. More like “SAS button pushing”.

The models I was building everyday were built in “Point-and-Click” software.By far, NSTAR was my most “Statistic-y” job, but Time-Series is one small part in the world of Statistics. I wanted to expand my horizons, and learned that there was A TON of opportunity in Analytics…Quick Overview of The Rest Of My Positions: Analytics Consultant, Silverlink Communications

Delivered market research, segmentations, research posters, and communication campaigns designed to support managed care organizations (MCOs), pharmacy benefit managers (PBMs), and disease management (DM) clients.

Analytics Manager, Vistaprint

Vistaprint sells business cards and other marketing products online. Their main customer base is small businesses.
Managed a team of analysts to optimize the Vistaprint website.
Held a bunch of other roles and work on a ton of different projects across Analytics

Senior Data Scientist, Constant Contact

Contant Contact offers email marketing solutions. Also Ecommerce, also targets small businesses.

I’ve been at Constant Contact now for 2 months. My first goals are:

Checking the validity of a model that is already in place.
Improving upon how they currently do testing. And then automating!
Trying to identify seasonal customers in their customer base.
Learning lots of new things!

A Note on Titles: Titles are tricky. A title may sound snazzy and not pay as much, and sometimes a lower title could pay more than you expect!As leveraging data for business purposes is becoming increasingly popular, there is even more confusion around what roles and responsibilities and skills would typically fall under a certain title. Explore all of your options!You can check out average salaries for titles on a number of different sites.

The Tools I Use (Starting From Most Basic):Everywhere I have been has used Excel. The ability to do:

Pivot tables
V-lookups
Write a simple macro using the “record” button to automate some data manipulations
These types of things can make you look like a WIZARD to some other areas of the business. (Not saying it’s right, just saying that’s how it is)
And I’ve used these things THROUGHOUT my career.

As data is getting bigger, companies are starting to move towards Tableau. I’m still new to it myself, but it has saved me from watching an Excel document take forever to save. I consider the days of waiting on large Excel files to mostly be just a thing of my past.

Data quickly becomes too large for Excel, I’ve found that anything higher than like 400k rows (with multiple columns) becomes a real chore to try and manipulate.
Pretty visualizations, can be interactive, quick, point-and-click.

Tableau can also take data in directly from SQL (a .csv, and a bunch of other formats as well).

Data Science example of a simple query

Data Science use the command line to access Hive

Data Science example of my Python code in JupyterLab

The real workhorse of a job in Data Science in SQL. It's becoming more common to pull directly to R or Python from SQL and do your data manipulation there, but this still requires connecting to the database.In school, most of the data was given to me in a nice form, all I had to bring to the table was analysis and modeling. In industry, you have millions of rows in 100’s or 1,000’s of different tables.

This data needs to be gathered from relevant tables using relevant criteria. Most of the time you’ll be manipulating the data in SQL to get it into that nice/useable form that you’re so familiar with. And this is time intensive, you’ll start to realize that a significant portion of your job is deciding what data you need, finding the data, transforming the data to be reasonable for modelling, before you ever write a line of code in R or Python.My last 3 jobs in industry have involved SQL, and I’ve only had 4 jobs.You can pull data directly from SQL into Excel or R or Python or Tableau, the list continues.

There are many different “flavors” of SQL. If you know one, you can learn any other one. In the past, I had been intimidated by job postings that would list APS or some other variant. There may be slight differences in syntax, but they’re really just asking you to know SQL. Don’t be intimidated!Below is an example of a simple query. I’m selecting some id’s, month, year, and the count of a variable “sends” based on criteria given in the “where” statement. The query also shows a couple table joins, denoted by “join”, and then I give the criteria that the join is on.Once you understand SQL, making the jump to BigData is not as daunting. Using Hive (also something that looked intimidating on a job description), is much like SQL (plus some nested data you might need to work with), you can query data from Hadoop.I use the command line to access Hive, but nice UIs are out there.

If you look closely, you’ll see my query here is just “select account_id from contacts limit 1;” all that says is “give me one account_id from the contacts table”, and it looks just like SQL.

When I was getting my Masters in Statistics, everyone was using R. Even some statisticians now are making the move to Python. Previously, all of my modeling has been in R, but I’m testing the Python waters myself!

I taught myself Python in Coursera, and I’m currently using it in my new job. That’s the beauty of the internet. Want to learn a new tool? Just go learn it, the information is at your fingertips.Below is an example of my Python code in JupyterLab. It brand-spanking new, and really my screenshot does not do it justice. You can read more about JupyterLab here: JupyterLab

A quick note. I put my Coursera classes I’ve taken under “accomplishments” in LinkedIn. It’s not a bad idea.

Things I Didn’t Know About Industry:

You might have some Opportunity for travel — Fun-ness of destination can vary

I’ve been to Vegas, Orlando, Barcelona, Windsor Ontario, NJ and MD for Work.

There is typically budget for personal development

A book you want to read that is relevant? You can probably expense it.
A course on Coursera that is relevant? You can probably expense it.
They’ll send you to conferences sometimes
- Was at the Jupyter Pop-up March 21st and I’m attending the Open Data Science Conference in May.
  1. Don’t be shy about asking your boss if there is budget available.
    - To most it looks like you care about and are invested in your career!

Layoffs are a thing. I recently just learned about this first hand. And my experience was great.Vistaprint decided to downsize by $20m in employee salaries (182 people).

I got a pretty sweet severance package.
Tip! You can collect unemployment and severance at the same time!

This was the first opportunity I had in years to really think about the culture, direction, and really think about my next move.Vistaprint paid for a Career Coach that helped me with:

resume (they updated both my content and formatting).
Cover letter tips (description below)
Networking
Interviewing
Negotiating!

I literally took the requirements from the job and pasted them on the left. Then took my qualifications from my resume and posted them on the right. Took less than 15 minutes for each cover letter.

Interviewing

To read my more in-depth article about the in person interview in data science, click here

To read my more in-depth article about the job hunt in data science from the first application to accepting a job offer, click

here

The biggest takeaways I learned from the coach and my own experience interviewing for a Data Scientist position were…

Practice answering questions in the STAR format.

https://www.vawizard.org/wiz-pdf/STAR_Method_Interviews.pdf

In one phone screen (with Kronos), I was asked all of the questions I had prepared for:

Tell me about a time you explained a technical result to a non-technical audience?
Tell me about a time you improved a process?
Tell me a time about working with a difficult stakeholder, and how it was resolved?

TWO DAYS in a row, with different companies (one of them was Spotify), I was asked to answer FizzBuzz.

Be ready for an entry level coding problem or SQL problem is the job description asks for one of those skills.
FizzBuzz: http://rprogramming.net/fizz-buzz-interview-test-in-r/

Prepare talking about one of your projects in a way that the person interviewing you (who may have little context) is able to understand. High Level, focus on outcomes. Seriously, before you start talking about the project, describe what the objective was, it’s really easy to dive into something and not realize the other person has no idea what you’re talking about.I could really keep talking forever about the topics listed above, but wanted to give a brief overview hitting a bunch of different pieces of my experience. Maybe I’ll need to elaborate more later.Thank you for reading my experience. I hope you have great success navigating your way into the field of Data Science. When you get there, I hope you find it fulfilling. I do.