Senior Data Scientist
Boston, MA · Hybrid · Permanent
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What the market looks like
Senior data science hiring has split cleanly in two — one track focused on classical ML and statistical modeling, and a second focused on generative AI, evaluation, and applied research. Mid-senior compensation generally lands in a $145K–$275K base band, with the strongest concentration of roles across technology, financial services, and the more data-mature parts of healthcare. The companies hiring these profiles tend to have real engineering muscle on the other side of the fence, which is part of why the strongest senior data scientists we place are the ones comfortable moving across both tracks — a shift that reflects how the most serious organizations are now structuring their data science function.
Job responsibilities
Lead end-to-end data science projects from problem framing through deployment, with clear ownership of business outcomes
Translate ambiguous business questions into tractable analytical problems — and push back on the ones that are better answered another way
Design and run rigorous experiments, including A/B tests, quasi-experimental approaches, and causal inference where appropriate
Build and ship production-grade ML models in partnership with data engineers, ML engineers, and product managers
Set and enforce the team's standards for modeling methodology, evaluation, reproducibility, and code quality
Partner with product and engineering leadership on technical direction, roadmap priorities, and capability investments
Mentor junior data scientists through code reviews, design reviews, and structured feedback
Contribute to hiring by interviewing candidates, shaping assessment rubrics, and raising the overall team bar
Communicate findings clearly to stakeholders at every level, from engineers to C-suite, in both writing and in person
Candidate requirements
5+ years of data science experience shipping models or analyses that drove real decisions
Strong Python (pandas, scikit-learn, PyTorch or TensorFlow) and SQL
Deep statistical foundation — causal inference, experimentation, and Bayesian methods as appropriate
Track record of partnering with engineering to deploy production ML
Excellent communication — equally comfortable with engineers, PMs, and executives
Experience in technology, financial services, or similarly data-rich industries