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.