Data Science Resume User Guide

@jonathan.interviews Data Science Resume User Guide 

Introduction: 

Welcome! Thank you for your purchase of my data science and analytics resume template. I hope you’ll find this to be useful and worthwhile information to improve your applications. 

There are two critical components of a resume: (i) the content, and (ii) how it’s presented. This template can help with the presentation but of course the content is yours alone, and your background and experience should be the most important determinant of getting recruiter calls. However, there are also many candidates with relatively similar-sounding experience, and among those, presentation can make an enormous difference in response rates. 

I transitioned to data science in 2019. Since then, I’ve received senior data science offers from Amazon, Stripe, Uber, Twitter, Robinhood, Airbnb, and more. More to the point of this resume, I’ve received recruiter calls from cold online applications without referrals from the above companies as well as Meta, Netflix, Google, Spotify, and Doordash. 

Admittedly, my own resume has content; however, I’ve found that I’ve had better interview experiences than many folks with similar or stronger skill sets, suggesting the presentation in this template is very strong – at least for data science and analytics positions. Below, I describe the different sections and my thoughts on how to use the template to fill with your own content. In addition, be sure to follow me or ask questions on TikTok or Instagram @jonathan.interviews (currently more active on Tiktok).

Document Formatting: 

I’ve found it helpful to use tables to left-justify the company name, school name, and positions while right-justifying the dates and city-state locations. In the main template, I’ve made the borders 0pt and white to hide them, but I’ve included a version with borders so that it’s easier to see what’s going on. I also included a screen recording demonstrating multiple ways to remove the borders (same steps to put them back, just use black and 0.5pt instead of white 0pt). 

The document currently uses size 13 font for the subject headers and size 11 font for the details. Feel free to adjust this one size larger if your resume is scarce or one size smaller if you’d like to fit in more details, but I wouldn’t go beyond that range. 

Document Length: 

The standard resume length is one page, though my own has been about 2 pages for years. There’s nothing wrong with a second page so long as the additional information is meaningful and additive. If your earlier work is less relevant or the same style of work you do now, but at a junior level then it can be listed in 1-2 lines if at all, doesn’t require bullet points, and shouldn’t be the difference between a one-page and two-page resume. On the other hand, if your past two positions are data science related and earlier work is additive–consulting, marketing, or demonstrates some other important skill–then that could be worth extending to a second page. 

Additionally, if you have older experience that demonstrates a high-level of expertise and leadership such as conference presentations or publications, that could also be worth a second page. This is especially relevant for PhDs that transitioned into data science and data analytics.

Sections: 

The critical sections are education, technical skills, and employment history. That said, if your education and employment history aren’t enough to sufficiently demonstrate that you can do – or have done – the type of work in your target roles, then you may consider including additional sections.

Projects can include work done in school, in bootcamps, or on your own. For example, if you don’t have data science experience, but are currently in a master’s of data analytics or data science program, then you’ll likely have a capstone project. 

For that project – or any you might select in a bootcamp or on kaggle – don’t think about your grade. Rather, you want to demonstrate you have both the business acumen and technical skills to effectively add value and complete projects in your target roles. 

I’ve also added a hobbies/activities/leadership section in the past as well, so long as it’s filling out the last page and not adding an additional one. Sometimes, if you have a passion hobby – e.g. music, art, dancing, sports – and you’ve either invested a lot of time training or have decent accomplishments, that can help build a personal connection with interviewers or add some value that you might be a good teammate. 

Header: 

I include my name, email, and phone number. Your email should almost certainly be a gmail account or possibly a business account. It should not have cute usernames or nicknames, but rather be pretty close to your name. If you have a very common first and last name, try to add something or make a variant that still appears professional. Older email addresses like yahoo, hotmail shouldn’t hurt too much, but it can make interviewers eye roll a bit. 

Education: 

If you’re a recent grad with very high grades, consider including your GPA on a 4.0 scale. Personally, I would clear away from showing anything above 4.0 in the numerator because it ends up being not very precise (if you can get higher than 4.0 then technically that’s not the correct scale), and it would make me question exactly what the true potential scale could be. 

If space is an issue, you can remove degrees that don’t add additional value: e.g. removing high school or an associates degree after you’ve received a bachelors. I would generally keep the bachelors in there if you’ve gone on to get a graduate degree and show both, but if space becomes an issue, you could consider dropping it.

Technical skills: 

The most common technical skill I see in data science interviews by far is SQL. But, it doesn’t take a lot to learn the basics, which I describe on my tiktok channel @jonathan.interviews. Make sure you have this in multiple places on your resume. 

After that, I organize a few different skills: coding and production skills on one line (you may learn spark, airflow, and git commands on the job but most entry-level analyst and DS roles shouldn’t require these). Then other lines for different paths in data science: experimentation, machine learning. You could also have a line for more engineering-related items if you’re more in the data science engineering track. 

Career Experience: 

Career experience is where I see the most mistakes in resumes. In general, if you’re going for data science and data analyst roles, you want to demonstrate: 

• Technical skills, 
• Business acumen and communication, 
• Impact 

Minimize the use of vague things that could be read as not requiring a technical skillset. For example, “responsible for communicating with multiple stakeholders” could be interpreted positively as explaining technical concepts and business impacts to department leads, or negatively as having a few zoom calls and summarizing descriptive statistics. 

As in interviews, your career experience should attempt to follow the STAR method as much as possible: Situation-Task-Action-Result. 

Consider the previous example, “responsible for communicating with multiple stakeholders,” which I’ve seen in many resumes. What exactly was the problem the candidate was trying to solve? What did they do, and what was the impact? As an interviewer or recruiter, I have no idea from this line what the applicant was working on. 

Compare that to an example in the resume template: “Designed and analyzed experiments to estimate the impact of repricing legacy free products, driving $2.5 million incremental revenue per year.” We know the situation: products were free and now the business is considering repricing these products. The task was to estimate the impact of a repricing. The action was designing and analyzing an experiment, and the result was a meaningful increase in revenue for the company. 

Personal Projects: 

There is a vast difference between classroom and practical experience. Relevant practical experience provides the most confidence for interviewers that you can do the job well if you’ve done it before. In the case where you have limited related career experience, you can and should bolster your credentials with projects. 

If you’re currently in a master’s program and considering a capstone project, expect that discussing this project may be the bulk of your interviews, so choose one that you’ll be excited to talk about and demonstrates a relevant skill set. 

Presentations and Publications: 

Relevant mostly for PhDs, but if you’ve presented work in conferences or had publications, this is often worth listing. Even if less relevant, presentations and publications suggest that you’ve been able to gain a level of expertise in your focus of study, which is a great signal. 

Hobbies, Activities, and Leadership: 

This tends to be more relevant for recent undergraduates, but if your resume is scant and you have some interesting leadership roles or passionate hobbies and activities, this can be worthwhile to add. It may demonstrate soft skills that you can be a team player for example, or simply provide a casual talking point to break the ice with interviewers. 

This may be less useful for larger companies: some tech companies standardize interview questions to remove potential biases, but smaller companies may have higher appreciation for personal connections.