Data Scientist Job Description Template (Free, 2026)

Job Title: Data Scientist [Mid-Level / Senior / Staff] Department: Engineering / Data Science Location: [City, State] / Remote / Hybrid Reports To: Head of Data Science / VP Engineering Employment Type: Full-Time About [Company Name] [Company Name] is a [stage/sector] company with access to [describe data: X events/day, X GB/day, behavioral signals, transaction data]. Our data science function directly influences product strategy, pricing, and growth decisions. We invest in a modern data stack — [Snowflake / BigQuery / Redshift], [dbt], [MLflow / SageMaker / Vertex AI] — to ensure our data scientists spend time on insight generation, not infrastructure. About the Role As a Data Scientist, you will turn raw data into business intelligence, predictive models, and recommendation systems that drive measurable outcomes. You will work embedded with [product / growth / operations] teams, owning end-to-end data science workflows from exploration and hypothesis formation to model deployment and A/B testing. Your models will run in production serving [X] users. Key Responsibilities • Formulate and test hypotheses on user behavior, conversion, retention, or [domain-specific] patterns using statistical methods • Build, validate, and deploy ML models for [use cases: recommendation, churn prediction, fraud detection, demand forecasting] • Design and analyze A/B experiments to measure causal impact of product changes • Create self-service dashboards and analytical reports for business stakeholders • Collaborate with data engineering teams to define reliable feature pipelines for model training and serving • Maintain model performance over time — monitor data drift, retrain models, and manage model versioning • Communicate findings and recommendations clearly to non-technical stakeholders including executives • Contribute to the development of ML infrastructure: feature stores, experiment tracking, model registries • Mentor junior data scientists and help define team standards for code quality and reproducibility • Stay current with advances in ML/AI (LLMs, causal inference, Bayesian methods) and evaluate applicability Required Qualifications • [3]+ years of data science or quantitative research experience in industry • Proficiency in Python with the scientific stack (pandas, numpy, scikit-learn, statsmodels) • Strong statistical foundations: regression, classification, hypothesis testing, Bayesian inference • Experience with SQL and large-scale data platforms (Snowflake, BigQuery, Spark) • Hands-on experience training and deploying ML models in production • Experience designing and interpreting A/B experiments with statistical rigor • Ability to communicate complex results clearly to non-technical audiences Nice to Have • Experience with deep learning frameworks (PyTorch, TensorFlow) or LLM fine-tuning • Knowledge of causal inference methods (difference-in-differences, synthetic control, IV) • Experience with ML platforms (SageMaker, Vertex AI, Databricks MLflow) • Published research or Kaggle competition achievements What We Offer • Competitive salary: $[low]–$[high]/year (see benchmarks below) • Equity: [X]% stock options / RSUs • Health, dental, and vision insurance (100% employer-paid for employee) • Flexible PTO + [X] company-wide holidays • Remote-friendly / home office stipend of $[X] • Learning & development budget: $[X]/year including conference attendance (NeurIPS, ICML) • Access to GPU compute for personal research projects • [Additional perk — wellness stipend, etc.] Salary Range: $100,000–$185,000/year (US, 2026 benchmark; exact offer commensurate with experience) [Company Name] is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.