Data Scientist Job Description Template
Build predictive models and analytics pipelines that drive product decisions and customer insights. Own the full lifecycle from data exploration through production deployment.
No signup, no card. The tool fills the rest in for you.
Why hire a Data Scientist?
As we scale, manual reporting and gut-feel decisions cost us growth. We need someone to systematically find signal in our data and automate insights that help sales, product, and ops move faster.
Data Scientist salary ranges
Approximate annual gross salary bands (Q2 2026). Always adjust for your city, seniority, and the candidate’s experience.
United States
$110,000 – $165,000
United Kingdom
£85,000 – £130,000
Eurozone
€100,000 – €150,000
Data Scientist responsibilities
- Design and train machine learning models to solve specific business problems (churn prediction, pricing optimization, demand forecasting)
- Build data pipelines and dashboards that surface actionable insights to cross-functional teams weekly
- Partner with product and engineering to ship data features into the application (recommendation engines, anomaly detection, segmentation)
- Validate model assumptions against production data monthly; iterate and retrain when performance drifts
- Document methodology and results so non-technical stakeholders understand confidence intervals and limitations
- Reduce ad-hoc analysis requests by automating recurring reports and self-serve BI tooling
Skills & requirements
Required
- 3+ years building and shipping ML models in Python (scikit-learn, pandas, numpy); production experience required
- SQL fluency; able to write complex window functions and CTEs to construct training datasets
- Experience with a cloud data warehouse (Snowflake, BigQuery, or Redshift) or datalake
- Statistics fundamentals: hypothesis testing, confidence intervals, A/B test design—not just libraries
- Familiarity with one ML ops tool (Weights & Biases, MLflow, or equivalent) or version control for models
- Clear communication; able to explain model trade-offs and uncertainty to non-technical founders and managers
Nice to have
- Experience with time-series forecasting or causal inference methods
- Shipped a recommendation system or classification model in a production SaaS application
- Exposure to analytics engineering or dbt for data transformation
Copy-ready Data Scientist job description
Data Scientist [Company name] · [City], [Country] · [On-site / Hybrid / Remote] $110,000 – $165,000 (US) · £85,000 – £130,000 (UK) · €100,000 – €150,000 (EU) — gross/year
Build predictive models and analytics pipelines that drive product decisions and customer insights. Own the full lifecycle from data exploration through production deployment.
Why this role exists As we scale, manual reporting and gut-feel decisions cost us growth. We need someone to systematically find signal in our data and automate insights that help sales, product, and ops move faster.
What you'll do
- Design and train machine learning models to solve specific business problems (churn prediction, pricing optimization, demand forecasting)
- Build data pipelines and dashboards that surface actionable insights to cross-functional teams weekly
- Partner with product and engineering to ship data features into the application (recommendation engines, anomaly detection, segmentation)
- Validate model assumptions against production data monthly; iterate and retrain when performance drifts
- Document methodology and results so non-technical stakeholders understand confidence intervals and limitations
- Reduce ad-hoc analysis requests by automating recurring reports and self-serve BI tooling
What you'll need
- 3+ years building and shipping ML models in Python (scikit-learn, pandas, numpy); production experience required
- SQL fluency; able to write complex window functions and CTEs to construct training datasets
- Experience with a cloud data warehouse (Snowflake, BigQuery, or Redshift) or datalake
- Statistics fundamentals: hypothesis testing, confidence intervals, A/B test design—not just libraries
- Familiarity with one ML ops tool (Weights & Biases, MLflow, or equivalent) or version control for models
- Clear communication; able to explain model trade-offs and uncertainty to non-technical founders and managers
Nice to have
- Experience with time-series forecasting or causal inference methods
- Shipped a recommendation system or classification model in a production SaaS application
- Exposure to analytics engineering or dbt for data transformation
What we offer
- Salary: [range, gross, with currency and time unit]
- [Equity / bonus / commission if applicable]
- [Health, PTO, learning budget, equipment — only what's real]
- [Work mode + flexibility]
About [Company] [2–3 sentences: stage, customers, traction. Keep it specific.]
Want it tailored to your company and country?
The free generator writes a country-aware, inclusive, salary-formatted version in 30 seconds — then ranks the applicants when they roll in.
Frequently asked
What does a Data Scientist do?
Build predictive models and analytics pipelines that drive product decisions and customer insights. Own the full lifecycle from data exploration through production deployment. As we scale, manual reporting and gut-feel decisions cost us growth. We need someone to systematically find signal in our data and automate insights that help sales, product, and ops move faster.
What should a Data Scientist job description include?
A strong Data Scientist job post has a one-line hook, why the role exists, 6 outcome-led responsibilities, a clear list of required skills, the salary range, and a country-specific compliance line. Use the copy-ready template above as a starting point.
How much does a Data Scientist earn?
Approximate annual gross bands (Q2 2026): $110,000 – $165,000 in the US, £85,000 – £130,000 in the UK, and €100,000 – €150,000 in the Eurozone. Adjust for city, seniority, and experience.
How do I write a Data Scientist job description fast?
Use Penroll's free job description generator — enter the title and country and it produces a complete, inclusive, salary-formatted Data Scientist post in about 30 seconds, no signup required.
More Engineering job descriptions
Backend Developer
Own the design, build and scaling of server-side systems that power your product. You'll write clean, testable code and make architectural decisions that balance speed-to-market with long-term maintainability.
Data Engineer
You design and maintain the data pipelines and infrastructure that turn raw data into reliable, accessible datasets for analytics and product teams. You own data quality, performance, and the systems that make data work at scale.
DevOps Engineer
Owns the infrastructure, deployment pipelines, and reliability that keep the product online and shipping.
Engineering Manager
Lead a team of 4–8 engineers to ship features on time and maintain code quality. Own sprint planning, technical decisions, hiring, and performance feedback while staying hands-on with architecture.