Senior Manager, Data Science

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What the market looks like

We've tracked over 1,500 management-level data science postings in the US over the last six months, with hiring concentrated in California, New York, and Washington. Organizations across technology, financial services, professional services, and healthcare are actively building out data science teams, and compensation for this cohort ranges from $200K to $330K base salary. The strongest candidates in this market combine hands-on technical depth—usually 5+ years shipped work in predictive modeling, statistical analysis, or ML systems—with clear evidence of growing teams, defining roadmaps, and translating business problems into data-driven solutions.

Job responsibilities

• Lead the design, development, and deployment of data science solutions that solve high-impact business problems, from scoping to production monitoring

• Build and mentor a team of data scientists and analytics engineers, setting technical direction, conducting code reviews, and fostering a culture of rigor and experimentation

• Partner with product, engineering, and business stakeholders to translate strategic priorities into data science roadmaps and measurable outcomes

• Own model governance, validation, and performance—establishing best practices for reproducibility, documentation, and responsible deployment

• Drive adoption of new tools, frameworks, and methodologies that improve team velocity and model quality across the organization

• Communicate results and trade-offs clearly to non-technical audiences, translating statistical findings into business impact

• Contribute to hiring and retention, building a team that reflects diverse perspectives and brings complementary technical skills

Candidate requirements

• 5+ years of hands-on data science work, shipping models and analytical solutions in production environments

• 2+ years managing or leading data scientists, with demonstrated success in hiring, mentoring, and unblocking technical teams

• Strong foundation in statistics, machine learning, and feature engineering; fluency with Python or R and SQL

• Experience translating ambiguous business questions into scoped data science problems and communicating results to mixed audiences

• Comfort working in remote or distributed settings and collaborating across engineering, product, and business functions

• Track record of ownership—you've led end-to-end projects from problem definition through production impact and monitoring