Demand for data scientists is high. Employment growth numbers project a 35% increase by 2035, a rate far higher than the overage occupation. And with a noted talent deficit, data scientists have an excellent job outlook.
Still, that technical gap is crucial — you need extended skill to land the ideal job. Plus, a top-notch resume that exhibits your abilities to a recruiter is necessary, no matter how skilled you may be. Let’s explore the skillsets you need to include on your data scientist resume.
Why is it important to prepare an informative CV?
Your resume is your first impression. As more qualified individuals shift towards the open opportunities available in the industry, effective ways to demonstrate your own skill sets become valuable. If you can highlight your key competencies, you differentiate yourself from the crowd. In other words, for a job role defined by the ability to quantify data, be sure to quantify your own achievements. A well-organized, skill-based resume demonstrates the core values of the data scientist: attention to detail, efficiency, and defined insights.
Skills to include in your data scientist resume
So what elements should you include in your resume? That answer is situation-dependent. Many organizations now expect data scientists to know a wide variety of skill sets, from big data to cloud computing. The discipline continues to operate within different business verticals — the exact skills you need will depend on your unique career trajectory.
There are several essential fundamentals we suggest you include on your resume.
Must-have skills:
Core competencies focus on the manipulation and analysis of data:
- Data analysis: The act of exploring, cleaning, and interpreting data is the primary responsibility of a data scientist. Provide numerical evidence of how your efforts improved processes at an enterprise level (revenue earned, data set size, etc.).
- Data engineering: Data scientists manipulate data. That task is executed with programming languages (such as Python or R). Give examples on your resume of specific projects where you used programming tools to achieve project goals (Pandas, MatplotLib, etc.).
- Machine learning (ML): ML and its various specializations (deep learning, artificial intelligence, neural networks) can help data scientists synthesize large amounts of data. As the adoption of ML practices increases, so does its value on your resume. Include any specific algorithms you have used (e.g., linear or logistic regression).
- Statistics: The mathematics of probability and statistics are the fundamental theories of data science. Wrangling data relies on knowledge of probability distributions, Bayesian inferences, and model validations. Be sure to note these skills on your resume.
- Database management: Data scientists use tools to extract data and store it. To that end, include clear examples of competence with Structured Query Language (SQL), data retrieval and preprocessing, and database harmonizing.
- Data visualization: Data is useless unless it provides insight. It is your job as a data scientist to communicate the value of any collected data to all stakeholders. Note on your resume your approach to data interpretation, from pattern recognition to predictive modeling.
Nice-to-have skills:
The following additional skills are not mandatory (yet), but may be required for specific job roles. The qualifications demonstrate a deeper level of expertise, commitment, and adaptability, which can help you stand out from other data scientists.
- Cloud computing: Depending on the specificity of your role, this may be a “must-have” skill set. As enterprises move more into cloud infrastructure, knowledge about cloud platforms may be required in order to perform your job. Any proficiency in cloud environments is a bonus attribute.
- Business management: Most resumes focus on technical skills. But if you want to progress in your career, leadership ability and business acumen are excellent supporting material. Demonstrate how you used strategic planning, team management, and organizational structuring for success in past projects.
- Containerization: There is extended debate on whether or not containerization is a required skill set. Many consider knowledge of Docker indispensable in the field, especially as organizations want data scientists to bring models into production. While not necessarily a requirement, learning the basics of containerization is an easy way to boost your resume.
- Natural Language Processing (NLP): NLP is a specialized field within data science. And within industries that are more text-specific (ecommerce customer support), it has numerous applications. If you want to diversify your abilities or want more role specificity, NLP is a great option.
- Big data tools: Not all projects require big data tools or resources. Still, big data as an industry specialization is growing. Knowledge about tools such as Hadoop are often required, so consider including such skills on your resume.
What to include in the “About me” section
Most recruiters have limited time and quickly scan a resume. That's why you include an About Me section or a resume summary on your CV. It's your chance to “hook” the HR rep and entice them to explore the rest of your resume.
In a concise 2-4 sentences, include the following relevant information:
- Core competencies
- Years of experience
- Preferred tools and techniques
- Specializations
- Major accomplishments
- Personal attributes or career goals
What to include in the “Achievements” section
After defining your unique traits and abilities, you need to prove your claims. That's why you include an Achievements section. List out the quantifiable results of your past projects, including:
- Your role (project objectives, methods, techniques, best practices)
- Business impact (improvements, efficiency, cost savings, etc.)
- Challenges overcome (product changes, errors, deadlines, etc.)
- Innovations (developed algorithms, models, methodologies)
- Recognitions (awards, accolades, customer satisfaction, etc.)
Data scientist resume templates by seniority
Resume sample #1: Junior data scientist
NAME SURNAME Data Scientist SUMMARY:
TECHNICAL SKILLS: Engineering practices:
Technologies:
WORK EXPERIENCE (SAMPLE PROJECT DESCRIPTION): [project / customer name] June 2022 - present Project Role: Data Scientist Customer Domain: Software & Hi-Tech Team size: 5 Responsibilities:
Database: PostgreSQL Tools: Python, Docker, Kubernetes, Git, AWS, Google Cloud Technologies: Tesseract, EasyOCR, PaddlePaddle. YoloV3-V5, Detectron2, EffNetDetection EDUCATION: BA in Economic Cybernetics, 2021 LANGUAGES: English B2 Lithuanian Native |
Resume sample #2: Middle-level data scientist
NAME SURNAME Data Scientist SUMMARY:
TECHNICAL SKILLS: Engineering practices:
Technologies:
Soft skills:
WORK EXPERIENCE (SAMPLE PROJECT DESCRIPTION): [project / customer name] Jan 2022 - present Project Role: Data Scientist Customer Domain: Life Sciences & Healthcare Team size: 5 Responsibilities:
Tools: Microsoft Azure, Azure Machine Learning, Azure Cognitive Services, Azure DevOps, Visual Studio, Visual Studio Code, Microsoft Excel, R Studio EDUCATION: PhD in Business Data Analysis, 2020 CERTIFICATIONS: Microsoft Certified Azure Data Scientist & AI Engineer (2020) LANGUAGES: English C1 Hungarian Native |
Resume sample #3: Senior data scientist
NAME SURNAME Senior Data Scientist SUMMARY: Tech-savvy data scientist with a sharp focus on data-centric projects:
TECHNICAL SKILLS: Engineering practices:
Technologies:
Leadership & soft skills:
WORK EXPERIENCE (SAMPLE PROJECT DESCRIPTION): [project / customer name] June 2022 - present Project Role: Solution Architect Customer Domain: Life Sciences & Healthcare Team size: 10 Responsibilities:
Tools: Azure ML, DVC, MLflow, papermill, LightGBM, responsible-ai-toolbox, Streamlit EDUCATION: MA in Computer Science, 2014 LANGUAGES: English B2 Ukrainian Native |
Resume sample #4: Lead data scientist
NAME SURNAME Lead Data Scientist SUMMARY: Data scientist with 15+ years of experience in converting data to value with data science, machine learning, and AI. Excited about making the world better with the help of technology. Key areas of technical expertise:
TECHNICAL SKILLS: Engineering practices:
Technologies:
Leadership & soft skills:
WORK EXPERIENCE (SAMPLE PROJECT DESCRIPTION): [project / customer name] July 2020 - present Project Role: ML team lead Customer Domain: Manufacturing Team size: 10-15 Responsibilities:
Tools: TensorFlow Object Detection API, pandas, OpenCV, Python, Docker, AWS EC2, AWS S3, Amazon Mechanical Turk, Amazon Recognition Custom Labels, OpenAI API Technologies: Deep Learning, Objects Detection, Images Classification, Generative AI EDUCATION: MA in Applied Physics and Mathematics, 2015 CERTIFICATIONS: AWS Certified Machine Learning – Specialty (2020) Microsoft Certified Azure Data Scientist & AI Engineer (2020) LANGUAGES: English C1 Spanish Native |
Apply for a data scientist job at EPAM Anywhere
Data scientists have a future full of opportunities. The industry is growing, and that growth supports exciting projects with competitive salaries. You apply for one of our remote data scientist jobs today. And with a well-built resume in hand, you will be well on your way toward an exciting career in data science.
As Chief Editor, Darya works with our top technical and career experts at EPAM Anywhere to share their insights with our global audience. With 12+ years in digital communications, she’s happy to help job seekers make the best of remote work opportunities and build a fulfilling career in tech.
As Chief Editor, Darya works with our top technical and career experts at EPAM Anywhere to share their insights with our global audience. With 12+ years in digital communications, she’s happy to help job seekers make the best of remote work opportunities and build a fulfilling career in tech.
Explore our Editorial Policy to learn more about our standards for content creation.
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