Data Scientist Job Description (2026): Duties, Skills and Salary
A data scientist uses statistics, machine learning and knowledge of the business to help it make better decisions. Typical work includes designing and analyzing experiments, building predictive models such as churn or demand forecasts, and finding the patterns in data that explain why a metric moved.
The role varies a lot between companies. Product data scientists concentrate on experiments and metrics; others build models that go into production with machine learning engineers. Most work in Python or R with SQL, and spend as much time framing questions and explaining results to non-technical colleagues as they do modeling.
Data Scientist job description template
Copy it, replace everything in [brackets] and cut what doesn't apply. Keep the requirements to what the job really needs.
Template · replace the [brackets]
Data Scientist
[Company] · [City and office days, or remote with the countries or time zones you hire in] · [Full-time]
About [Company]
[Two or three sentences on what your company does, who it serves and why this team matters now.]
About the role
We're looking for a data scientist to help [team or product] make better decisions with data. You'll design experiments, build models for [problem, e.g. forecasting demand or predicting churn], and turn results into recommendations leaders can act on.
What you'll do
- Design A/B tests with product teams, define success metrics and analyze results with appropriate statistics.
- Build predictive models in Python [scikit-learn, XGBoost, statsmodels] for [use case].
- Explore data to explain changes in key metrics and to find opportunities.
- Work with data engineers on reliable features, and with ML engineers on deploying models.
- Write up findings and present them with clear caveats and a recommendation.
- Define core metrics so every team measures success the same way.
- Review other data scientists' analyses for correctness.
What you'll bring
- [X]+ years of applied data science or quantitative analysis.
- Strong statistics: hypothesis testing, regression, experimental design and the basics of causal inference.
- Proficiency in Python or R, plus SQL.
- Experience building and validating machine learning models on real business data.
- The ability to turn a vague business question into an analysis plan.
- Clear communication of uncertainty to non-technical audiences.
Nice to have
- A graduate degree in statistics, economics, computer science, physics or another quantitative field.
- Causal inference methods such as difference-in-differences or synthetic control.
- NLP or large language models applied to text data.
- Domain experience in [industry].
What we offer
- [Pay range: e.g. $X–$Y base salary, plus bonus or equity if offered]
- [Conference budget, time for research, flexible hours]
- [Health cover, retirement or pension plan, paid time off, parental leave]
- [Work authorization: whether you can sponsor a visa or hire through an employer of record]
[Our process: a recruiter call, a statistics and SQL interview, a case study on a realistic business problem presented to the team, and a meeting with the product leads you'd support.]
[Your equal opportunity statement, and how candidates can ask for adjustments during the process]
Put the pay range in the ad. Several US states, including California, Colorado, New York and Washington, require it in job postings, and the EU Pay Transparency Directive gives applicants the right to know the starting pay or its range before the interview.
What real data scientist job ads ask for
The skills named most often in the 489 newest data scientist job ads we analyzed (out of 2,450 live postings), as the share of ads that mention each. A skill most ads name is a fair must-have; one few ads name belongs under nice-to-have.
- 1Python81%
- 2Machine learning74%
- 3SQL59%
- 4Statistics50%
- 5LLMs38%
- 6Data analysis36%
- 7R24%
- 8AWS23%
Degrees mentioned most: Computer Science (37%), Bachelor's degree (34%), PhD (23%).
From live job ads · computed November 2, 2026 · refreshed daily
Data Scientist duties by seniority
What the job usually looks like at each level. Pitch the ad at one level and say which in the title.
Junior
- Runs analyses and models for a defined question, with close review.
- Cleans data and keeps notebooks reproducible.
- Presents findings to their own team.
Mid-level
- Owns analytics and models for a product area or business function.
- Designs experiments and chooses methods independently.
- Changes decisions through stakeholders across teams.
Senior
- Sets the data science agenda for a domain with business leaders.
- Brings in new methods and reviews the team's statistical rigor.
- Mentors data scientists and shapes hiring.
Data Scientist career path
- Junior Data Scientist
- Data Scientist
- Senior Data Scientist
- Principal Data Scientist or Data Science Manager
Many data scientists hold degrees in statistics, economics, physics or computer science; others move up from data analyst roles by adding modeling skills. Later paths include machine learning engineering, research science, analytics leadership and product roles where quantitative judgment matters.
Advertised pay for data scientist jobs
Median advertised base salary in Data Scientist job ads in the United States: $186,300 (middle half: $150,000–$225,000), from 522 job ads with a published salary in the last 90 days.
This is advertised pay, not total compensation: bonuses, commission, equity and benefits aren't included, and many ads publish no pay at all.
- Data Scientist salary in the United States · median $186,300, 522 ads
- Remote Data Scientist salary · median $187,500, 284 ads
Similar roles
- Data Analyst job description
Concentrates on reporting and descriptive analysis rather than modeling.
- Machine Learning Engineer job description
Builds the production systems that serve models at scale.
- Data Engineer job description
Builds the pipelines and tables data scientists work from.
- Financial Analyst job description
Forecasts and models financial results, mostly in spreadsheets and planning tools.
- Business Analyst job description
Turns business needs into requirements and process changes.
Data Scientist job description FAQ
What does a data scientist do?
Uses statistics, machine learning and business knowledge to answer questions and predict outcomes: designing and analyzing experiments, building models such as churn or demand forecasts, and explaining results so teams can act on them.
What's the difference between a data scientist and a data analyst?
Analysts mostly describe what happened, through reporting, dashboards and analysis; data scientists add statistical modeling, experimentation and prediction. In practice the line blurs, so a job description should say which work dominates.
Do data scientists need a PhD?
Rarely. Many job ads list a master's or PhD as preferred, especially in research-heavy teams, but most applied roles hire on demonstrated skill in statistics, coding and communication.