Do I need it to say PhD on my resume to get a Data Science interview?


Do I need it to say PhD on my resume to get a Data Science interview?

Many times I've been told by a recruiter that I wasn't qualified for the job because I don't have a phD (even though the job listing often says an MS is sufficient)…

You’re about to graduate with an MS in STEM and you’ve completed coursework and personal projects involving data sets and machine learning. You were feeling pretty good about your chances of landing a Data Science role when you hit a wall - you’ve been applying for Data Scientist positions for well over 6 months. You’re hundreds of applications deep, and you just have a few phone screens to show for it… You’re starting to wonder “Do I need it to say PhD on my resume to get a Data Science interview”?

While the Data Scientist job market is competitive, you don’t need a PhD to land an interview - as long as you’re going about the process the right way. Specifically -

First make sure you are applying to the right roles for your background.

  • It may sound very very obvious, but first check a PhD is NOT explicitly asked for on the Job Posting (if it is, it is not impossible to land an interview, but you’re going to face an uphill battle)

  • Even if a PhD is not asked for, it is worth doing a couple of further checks, to avoid setting yourself up for unnecessary rejection emails.

    1. Team Profiling: Check if the current team has PhDs. If they do - and you can check fairly easily by searching on LinkedIn for Data Scientists at a given company - that is a red flag that they may have a bias towards PhD candidates. You especially want to check for people with the same title you’re applying for and most recent hires - these roles are the most indicative of whether you’ll need one to be in the consideration set

    2. Job Profiling: Compare the Job Posting requirements versus the Data Science Venn Diagram. The greater the skew towards and/or total number of requirements in the Maths & Statistics or Hacking segment, the greater the chance of it being a more deeply technical role and hence PhD level skills could be expected (needed) from the outset. Similarly, the more specific the skills asked for, the greater the chance of PhD requirements - for example, reference to “predictive modeling” may be less likely to be PhD level versus a job posting asking for a list of more specific skills e.g., “Naive Bayes, RNNs, Anomaly detection”. (Note: More skew / number / specificity of requirements in the Domain segment likely implies some level of relevant industry experience is a pre-requisite). Note, this is not hard and fast rule, but this exercise is also helpful to get a feel for the relative balance of skills required for the role and whether this is a good fit with your skill-profile.

    3. Personal Profiling: Ensure the main job requirements / activities (or at least a decent subset of them) are things you can actually do - this may seem silly, but we’ve seen lots of examples of “spray and pray” approaches, where candidates are sending applications to as many places as possible, without really thinking through whether they have some of the core skills needed for a given role; or, not tailoring their applications accordingly - which brings us to our next point!…

Secondly, you need to make sure you have tailored your application to the role … you may be up against people who do have PhDs (even if the role doesn’t state/imply you require one), so what have you done to make sure your most relevant strengths stand-out? For example, have you

  • Listed relevant courses/coursework from your STEM background that are applicable (or, at a minimum, showcase your quant abilities)
  • Included any Internship positions that had a data-oriented component
  • Pulled out softer skills you’ve gleaned that are also critical to the role
  • Taken the time to research the company/team and hence write a compelling cover letter - really thinking through the role of the Data Science team and (a) why that appeals and (b) what strengths you can bring to the team on Day 1

Thirdly, you need to complete - and then clearly highlight! - Projects where you’ve used skills/techniques/programming languages etc. that the role requires. The only way you can truly eliminate doubts as to whether your skill set matches up versus a PhD candidate is to be able to show what you’re capable of - and in skills that matter to the Hiring Manager. If you don’t have a PhD then take special care to show that you’re sound on the mathematical concepts behind various algorithms as this can often be perceived as the gap between PhDs and non-PhDs - for example, add a “techniques used and why” section to your read.me write-up on Github to demonstrate your understanding of the math as well as how to apply it.

Overall, there are many ways you can improve your resume and cover letter to compete effectively in the Data Scientist job market. You don’t need a PhD for many roles; but you do need

  • A well thought through application strategy
  • To put time into tailoring your resume and cover letter
  • To be building up a portfolio of relevant projects that prove your capabilities

How to take action now!

Let’s practice the Team Profiling. Take the Job Posting you’re currently applying to (or, if you’re not there yet, pick one at random from a jobs site like indeed.com). Create the following column headings in a spreadsheet (or piece of paper):

  • Name
  • Title (e.g., Data Scientist, Data Analyst)
  • Level (e.g., Junior, Senior, Lead)
  • Undergrad Degree
  • Masters Degree
  • PhD
  • URL (useful to save it as you’ll need to come back to this research as you tailor your resume)

Now, use a search on LinkedIn to find “Data Scientists” at the Company. (If you’re struggling to come up with any, try the tips we lay out in this post). For each name, fill in the row of details - as much as you can find/is relevant to any given individual. Complete this for “Data Analysts” and “Data Engineers” too, to make sure you have as full a picture as possible of the data-related roles and team pyramid. Look at the results - how prevalent are PhDs? If they are abundant, especially among more junior and/or most recent hires, there’s a high chance you’ll need one too unless you have very compelling project work; if not - get tailoring that resume and apply! :)


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