Senior Data Scientist

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What the market looks like

We've tracked 3,100+ mid-level data science postings across the US in the last six months, with heavy concentration in California, New York, and Washington. Technology, financial services, and professional services drive the bulk of hiring, though demand spans healthcare, manufacturing, and life sciences as well. Median compensation for this cohort lands around $170,000, with strong performers reaching $300,000 at the top end. The strongest candidates in this space combine hands-on modeling expertise with the ability to translate business questions into analytical frameworks, ship end-to-end projects, and partner effectively across engineering and product teams.

Job responsibilities

• Own end-to-end data science projects from problem definition through model deployment, working with stakeholders to clarify business objectives and translate them into measurable outcomes

• Build, train, and optimize machine learning models using appropriate techniques for classification, regression, forecasting, or clustering problems; evaluate trade-offs between model complexity, interpretability, and performance

• Design and execute experiments (A/B tests, causal inference) to validate hypotheses and measure the impact of data-driven recommendations on business metrics

• Develop data pipelines and feature engineering workflows that support model development and production systems; collaborate with engineers on reproducibility and scalability

• Communicate findings through visualizations, dashboards, and presentations tailored to both technical and non-technical audiences; distill complex analyses into actionable insights

• Partner with product, engineering, and business teams to identify high-impact analytical opportunities and prioritize work based on strategic value

• Maintain best practices around data quality, model governance, and documentation; contribute to the team's analytical standards and tooling

Candidate requirements

• 4–7 years of experience in data science, analytics, or a quantitative role, with demonstrated ability to deliver complete analytical projects in production environments

• Strong foundation in machine learning concepts and statistical methods; experience building and evaluating models across classification, regression, or time-series problems

• Proficiency in Python or R, SQL, and standard data manipulation and visualization libraries; comfort working in cloud data platforms (AWS, GCP, Azure) or equivalent

• Experience designing and interpreting experiments (A/B tests, causal inference methods); familiarity with business metrics and how to measure analytical impact

• Demonstrated ability to communicate technical work to non-technical stakeholders; experience translating business questions into analytical approaches and back again