Data & Benchmarks · Data Scientist
Data Scientist Skill Benchmarks (2026)
What do real Data Scientist portfolios contain? Aggregated data from 56 Data Scientist portfolios on Seera: the top 10 skills grouped by category, GitHub/live-link rates, the practices they emphasize, and how many show quantified results.
Based on 56 real Data Scientist portfolios published on Seera, aggregated and anonymized. Last updated September 21, 2026.
- Portfolios skew business-intelligence over deep ML — Power BI (57%) ranks alongside Python (79%), while deep-learning frameworks barely appear.
- Advanced degrees are the exception: only 14% list a master's and 0% a PhD.
Skills by category
Share of Data Scientist portfolios that list each skill, grouped by type (spelling variants merged — see methodology).
Programming & Querying
BI & Visualization
ML & Data Libraries
Portfolio composition
What Data Scientist portfolios actually link out to.
What they emphasize
Share of portfolios that reference each theme, in their listed skills or descriptions.
Quantified results
41% of Data Scientist portfolios state at least one quantified result in their write-ups — a percentage, dollar figure, multiplier, or ROI/CTR/conversion metric.
Education
Degrees referenced in Data Scientist portfolios.
Years of experience
Distribution of self-reported experience across 56 portfolios.
Seniority mix
Key takeaways
- 79% of Data Scientist portfolios list Python — the single most common skill.
- The most common tech stack is Python, SQL, Power BI, Pandas, Git.
- The typical Data Scientist portfolio features 2.7 projects.
- Average experience across these portfolios is 2.0 years.
- Data Scientists list 2.0 roles in their work history, on average.
See it in a portfolio
To see these patterns in a finished portfolio, walk through our data science portfolio example, which breaks down each section from the hero to the case studies. If your work leans toward SQL, BI dashboards and experiments rather than modeling, compare it with this example of an analyst portfolio built around decisions, then use these three project templates for analysts to write up your own projects.
Methodology
These benchmarks are computed from 56 Data Scientist portfolios created on Seera, where users upload a resume and our AI extracts a structured profile (skills, projects, experience). We group portfolios by role from the stated professional title. Skill percentages merge spelling variants (e.g. React/React.js, CSS/CSS3, Adobe Photoshop/Photoshop). Theme percentages ("What they emphasize") count a portfolio that references a topic anywhere in its listed skills or descriptions — they measure what portfolios mention, not verified practice. All figures are anonymized aggregates; no individual portfolio, name, or contact detail is exposed. Free to cite under a CC BY 4.0 license with attribution to Seera.
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