Senior Data Scientist

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What the market looks like

Over the last six months, we've tracked more than 21,000 specialist-level postings in data science across the US, with heaviest concentration in California, New York, and Washington. The strongest candidates combine deep technical fluency in statistical modeling and machine learning with the ability to translate complex analysis into business impact—they ship real projects, work cross-functionally with engineering and product teams, and can operate both in exploratory analysis and production ML pipelines. Compensation for this cohort typically ranges from $170,000 to $310,000, with significant variation by sector and location. Top hiring sectors include technology, financial services, professional services, and healthcare.

Job responsibilities

• Own end-to-end data science projects from problem definition through model deployment and monitoring

• Build statistical and machine learning models to solve business problems, with attention to model quality, interpretability, and production readiness

• Partner with engineering teams to operationalize models and integrate them into live systems

• Conduct exploratory data analysis and experimentation to validate hypotheses and guide product and business decisions

• Communicate findings, trade-offs, and recommendations to non-technical stakeholders with clarity and rigor

• Mentor junior data scientists and contribute to team culture and technical standards

• Stay current with advances in machine learning, data engineering, and domain knowledge relevant to the business

Candidate requirements

• 5+ years of hands-on experience building and shipping statistical or machine learning models in production environments

• Strong foundation in mathematics, statistics, and algorithmic thinking; fluency with Python or similar languages and libraries (pandas, scikit-learn, TensorFlow, PyTorch)

• Demonstrated ability to work with data engineering and software engineering teams to move models from research to production

• Experience designing and running experiments, A/B tests, or similar validation frameworks to measure model impact

• Clear communication skills—comfortable translating technical work for business audiences and influencing decisions with data-driven reasoning

• Track record of taking ownership of projects and delivering results with minimal supervision