Even though 95 percent of employers say that data science and analytics skills are hard to find, candidates looking for a data scientist job still need to demonstrate their proficiency in core concepts and skills during the job-search process. A data scientist resume is a key element in telling this story.
A good data scientist resume should include a wide range of skills and qualities beyond the fundamentals of data science. “Data is intricate, and most likely, your account managers or creative teams won’t understand the technical complexities as you do,” explained Lauren Hamer, certified professional résumé writer and founder of LaunchPoint Résumé. That’s why hiring managers look for a mix of technical aptitude and communication skills (e.g., can you work with unstructured data, uncover ways to solve business problems, and present your findings to other stakeholders?), when reviewing data scientist resumes.
In this guide, we’ll cover the major steps to creating an effective data scientist resume, as well as delve into some of the things hiring managers look for in data scientists.
Characteristics of an Effective Data Scientist Resume
How can a data scientist go about showcasing their technical and communication skills in their resume? “Usually this is best done within the work history section,” Hamer noted.
In addition to presenting examples of your projects and work, the bullet points should mention the teams and stakeholders you worked with, as well as the medium you used to present the findings to others (detailed Excel reports, case studies, Agile project formats, Zoom, kick-off calls, presentation decks, etc.).
•Produced value-added deliverables for R&D and product marketing by mining and analyzing third-party and customer sentiment data to derive significant, tangible, actionable insights.
•Collaborated with data engineers and marketing team to implement ETL process, wrote and optimized SQL queries to perform data extraction to fit the analytical requirements.
• Designed rich data visualizations and views using combo charts, stacked bar charts, pareto charts, donut charts, geographic map, transforming the data with Tableau and Matplotlib.
• Delivered and communicated research results, recommendations, opportunities to non-technical managers and executive teams, via slide show over Zoom.
To further affirm the skill level of a data scientist and their ability to do their job, Hamer sometimes includes a quote or testimonial from a stakeholder or customer. For example:
“Lauren’s ability to see the bigger picture among the weeds helped our team launch a new product in just 3 months-something that wouldn’t have been possible without her detailed algorithms and statistical models.” -Bob Jones, senior R&D manager, ABC Company
Other Essential Elements
The other most vital sections to include in a data science resume are “technical skills” and “special projects.”
Hamer recommends placing your technical skills summary or toolbox in the top-one third of your résumé, right below your profile summary and before your work experience summary. It should list the platforms, programs, and languages you’re familiar with. (Bonus points for prioritizing the specific skills highlighted in the job description.)
While you should briefly mention projects in your work history bullets, Hamer explained, they are so important that they deserve their own section labeled “special projects” or “related projects” and a more detailed summary that will help draw the reader’s eye to them. Again, project summaries provide the perfect platform for explaining your approach, mindset, business acumen and the things that separate great data scientists from good ones.
With that in mind, here is a format for describing each project:
- Problem statement: What problem were you trying to solve and why? Also, to set yourself apart, briefly describe your approach, philosophy and analytical acumen.
- Who you worked with: Clarify your role and whether you were part of a team.
- Data: Describe the approximate size of the data set and the software, tools and techniques used to store, extract and clean the data.
- Models, algorithms and methodology: Specify models/algorithms and statistical techniques used, as well as programming languages and libraries used to construct them.
- Code: Consider linking to your GitHub account or portfolio so the hiring manager can review the code.
- Results and recommendations: Explain how you communicated the results of the analysis as well as the outcome or impact of your work.
Creating an Effective Summary Profile
An opening summary should include the target job title in the first line, then convey the candidate’s best value statement or differentiator— something unique.
Think back on feedback you’ve received from mentors, professors, supervisors, and so on. What do they love about you? Do you understand the big picture? Are you their most reliable worker they don’t have to worry about? Are you the one most trusted to train new employees? Mention those in your opening summary:
Goal-oriented Data Scientist offering a proven track record of understanding the business underpinnings of a problem, conducting effective analyses and delivering data-driven insights and value that improve decision making and positively impact the top and bottom lines. Highly motivated and insightful professional with exceptional intrapersonal and communication skills and over six years’ experience in data mining, extraction, analysis, statistical modeling, machine learning and data visualization. Turning data into valuable information is my passion and forte.
Final Tips and Characteristics of an Effective Data Scientist Resume
The length of your resume doesn’t matter quite as much as the content. Also…
Match the job description: To capture the attention of automated and human reviewers, make simple modifications/customizations to match the requirements in the job description—including the hard and soft skills, the organizational culture, industry expertise—before hitting ‘Send.’ Even better, use a free tool like Jobscan or Résumé Worded to compare your resume to a specific job description, make changes, add the right keywords, and get past applicant tracking systems.
Provide work samples: Provide a link to samples or a portfolio that reflect your work and are representative of how you work and communicate with technical and non-technical audiences.
Be sure to include certifications, coursework: You should include top certifications, as well as coursework and participation in hackathons and competitions that demonstrate expertise in must-have technologies and a passion for continuous learning. (In addition, more specialization and skills will allow you to potentially negotiate for a higher salary.)