Template and best tips for writing one

Even though 95% of employers say data science and analytics skills are hard to come by, candidates seeking data scientist jobs should still demonstrate mastery of basic concepts and skills during the process. job search. A data scientist resume is key to telling that story.

A good data scientist CV should include a wide range of skills and qualities beyond the basics of data science. “Data is complex, and most likely your account managers or creative teams won’t understand the technical intricacies the way you do,” explained Lauren Hamer, Certified Professional Resume Writer and Founder of LaunchPoint Resume. That’s why hiring managers look for a combination of technical and communication skills (for example, can you work with unstructured data, discover ways to solve business problems, and present your findings to others stakeholders?) when reviewing data scientist CVs.

In this guide, we’ll cover the main steps to creating an effective data scientist resume, as well as dive into some of the things hiring managers look for in data scientists.

Characteristics of an Effective Data Scientist Resume

How can a data scientist present their technical knowledge and communication skills in their resume? “Usually this is best done in the work history section,” Hamer noted.

In addition to showing examples of your projects and work, bullet points should mention the teams and stakeholders you have worked with, as well as the medium you have used to present the results to others (detailed Excel reports, studies cases, Agile project formats, Zoom, kick-off calls, presentations, etc.).

For example:

• Generating value-added deliverables for product R&D and marketing by extracting and analyzing third-party and customer sentiment data to derive meaningful, tangible and actionable insights.

• Worked with data engineers and marketing team to implement ETL process, writing and optimizing SQL queries to perform data extraction to meet analytical requirements.

• Design rich data visualizations and views using combo charts, stacked bar charts, Pareto charts, donut charts, geo maps, transforming data with Tableau and Matplotlib.

• Delivered and communicated research findings, recommendations, opportunities to non-technical managers and leadership teams, via Zoom slideshow.

To further affirm a data scientist’s skill level and ability to do their job, Hamer sometimes includes a quote or testimonial from a speaker or client. For example:

“Lauren’s ability to see the big picture among the weeds helped our team launch a new product in just 3 months, which would not have been possible without her detailed algorithms and statistical models.” -Bob Jones, Senior Director of R&D, ABC Company

Other Essentials

The other most important sections to include in a data science resume are “technical skills” and “special projects.”

Hamer recommends placing your technical skills summary or toolkit in the top third of your resume, just below your profile summary and before your work experience summary. It should list the platforms, programs, and languages ​​you know. (Bonus points for prioritizing specific skills highlighted in the job description.)

Although you should briefly mention projects in your work history bullet points, Hamer explained, they’re so important that they deserve their own section called “special projects” or “related projects” and a more detailed summary that will help draw the reader’s attention to them. Again, project summaries provide the perfect platform to explain your approach, mindset, business acumen, and what separates great data scientists from good ones.

With that in mind, here is a format to describe each project:

  • Problem statement: What problem were you trying to solve and why? Also, to stand out, briefly describe your approach, your philosophy and your sense of analysis.
  • Who you worked with: Clarify your role and if you were part of a team.
  • Data: Describe the approximate size of the dataset and the software, tools, and techniques used to store, retrieve, and clean the data.
  • Models, algorithms and methodology: Specify the models/algorithms and statistical techniques used, as well as the programming languages ​​and libraries used to build them.
  • Coded: 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 and the outcome or impact of your work.

Creating an Effective Summary Profile

An opening resume should include the target job title in the first line, then convey the candidate’s best value statement or differentiator – something unique.

Think back to the feedback you received from mentors, teachers, supervisors, etc. What do they like about you? Do you understand the big picture? Are you their most reliable worker that they don’t have to worry about? Are you the most trustworthy person to train new employees? Mention them in your opening summary:

Goal-oriented data scientist with a proven track record in understanding the business underpinnings of a problem, performing effective analytics, and delivering data-driven insights and value that improves decision-making and has a positive impact on results. Highly motivated and insightful professional with exceptional communication and communication skills and over six years of experience in data mining, mining, analysis, statistical modeling, machine learning and data visualization . Transforming data into valuable information is my passion and my forte.

Final Tips and Features of an Effective Data Scientist Resume

The length of your CV is not as important as its content. As well…

Match the job description: To grab the attention of both automated and human reviewers, make simple edits/customizations to match job description requirements, including hard and soft skills, organizational culture, industry expertise, before clicking “To send”. Better yet, use a free tool like Jobscan or Resume Worded to compare your resume to a specific job description, make changes, add the right keywords, and bypass applicant tracking systems.

Provide work samples: Provide a link to examples 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, courses: You should include top certifications, as well as courses and participation in hackathons and competitions that demonstrate expertise in must-have technologies and a passion for continuous learning. (Also, more specialization and skills will allow you to potentially negotiate a higher salary.)

Sean N. Ayres