Data Scientist résumé: what employers are asking for right now
We read 144 open data scientist postings on employers' own job boards, at companies including OpenAI, Lyft and Reddit. Of the 83 whose requirements we have read, the skills asked for most are Python (96%), SQL (95%) and Statistics (71%) — so those belong near the top of a data scientist résumé, in the words the posting uses. The median posting asks for 5+ years of experience.
From 144 open postings on employers’ own job boards, as of .
Skills data scientist postings ask for
| Skill | Share of postings | Bar |
|---|---|---|
| Python | 96% | |
| SQL | 95% | |
| Statistics | 71% | |
| Machine Learning | 57% | |
| Causal Inference | 35% | |
| Experimentation | 35% | |
| Data Visualization | 34% | |
| A/b Testing | 33% | |
| Product Analytics | 31% | |
| R | 24% | |
| Statistical Modeling | 23% | |
| Data Modeling | 18% | |
| Ab Testing | 16% | |
| Data Analysis | 16% | |
| Predictive Modeling | 13% |
Share of the 83 postings whose requirements we have read.
A skills section built from those postings
List only what you can talk about in an interview, and use the posting’s own words for it — a screener searching for “Python” will not match a synonym. Ordered by how often employers ask:
Skills
Python · SQL · Statistics · Machine Learning · Causal Inference · Experimentation · Data Visualization · A/b Testing
Product Analytics · R · Statistical Modeling · Data Modeling · Ab Testing · Data Analysis
Predictive Modeling
- Line 1: asked for by a third or more of postings.
- Line 2: often asked for.
- Line 3: sometimes asked for.
Sample data scientist résumé
An illustrative example, not a real person: the employers and results are made up. The skills are the ones the postings above ask for, so it shows how those requirements read on a page.
Senior Data Scientist
Senior Data Scientist with 5+ years building Python and SQL workflows for product analytics, experimentation, and statistical modeling. Experience turning data into causal, measurable decisions across A/B testing, machine learning, and data visualization.
Experience
Senior Data Scientist · Growth-stage consumer software company
2021 – present
- Built Python and SQL analysis pipelines for product analytics, reducing ad hoc reporting time by 40% and standardizing weekly decision metrics.
- Designed and analyzed A/B tests for onboarding and pricing changes, increasing trial-to-paid conversion by 6% across key customer segments.
- Developed causal inference and statistical modeling frameworks to isolate feature impact from seasonality, improving launch readouts for product teams.
- Created Looker dashboards and data visualizations that surfaced retention and funnel bottlenecks, enabling faster prioritization of experiments.
Data Scientist · Mid-sized fintech platform
2018 – 2021
- Developed predictive modeling and machine learning features for risk and growth use cases, improving model lift and supporting more targeted offers.
- Performed statistical analysis in Python, SQL, and R to evaluate campaign performance and customer behavior across acquisition channels.
- Implemented experiment analysis and Ab Testing templates that improved consistency in test design, power checks, and result interpretation.
- Modeled event and customer data for downstream reporting, strengthening data quality for product analytics and executive dashboards.
Skills
Python · SQL · Statistics · Machine Learning · Causal Inference · Experimentation · Data Visualization · A/b Testing · Product Analytics · R · Statistical Modeling · Data Modeling
More data scientist bullet points to adapt
- Applied causal inference methods to separate product effects from traffic shifts, giving leadership clearer guidance on which experiments to scale.
- Built machine learning models for propensity and retention use cases, improving targeting for lifecycle messaging and in-product recommendations.
- Created reusable A/b Testing analysis notebooks in Python and SQL, shortening experiment readouts and reducing manual QA.
- Translated complex statistical modeling output into concise dashboards for product, growth, and finance stakeholders.
- Partnered with data engineering on dbt-style modeling conventions to improve consistency across core product tables and metrics.
- Used R for deep-dive analysis on cohort retention and funnel behavior, uncovering drop-off patterns that informed roadmap changes.
- Designed data visualization views in Looker for north-star and input metrics, helping teams monitor performance without manual pulls.
- Validated product launches with experimentation frameworks that measured incrementality, guardrails, and downstream conversion impact.
What sets an application apart
Listed as a plus rather than a requirement: Machine Learning (14%), R (14%), dbt (13%), Generative AI (10%), Git (8%), Product Analytics (8%), Amplitude (7%) and Data Visualization (7%). Worth naming if you have it, and never worth claiming if you do not.
Level, location and pay
- Most are senior roles (61% of postings that state a level).
- 33% are listed as remote.
- Listed mainly in United States, United Kingdom and Canada.
- US postings that publish a yearly salary: median $269k, middle half $219k–$270k (from 18 postings).
Who is hiring data scientists
- OpenAI17 open
- Lyft12 open
- Reddit11 open
- Pinterest9 open
- Anthropic6 open
- Roblox6 open
- Fin5 open
- Lovable5 open
- Suno5 open
- Zoox5 open
- Databricks4 open
- Instacart4 open