Director, Data Science

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What The Market Looks Like We've tracked over 800 senior-level data science postings across the US in the last six months, with the strongest concentration in California, New York, and Washington. Technology, financial services, and professional services firms are hiring most actively at this level. Senior data science leaders typically see compensation in the $230k–$410k range. The directors we work with bring deep technical credibility—usually 8+ years of hands-on ML and analytics work—combined with the ability to set team strategy, mentor mid-career data scientists, and translate complex model work into business outcomes.

Job Responsibilities

• Own the data science team's roadmap and deliverables—defining which models and analyses drive the most business value and ensuring on-time, high-quality ship
• Build and mentor a team of 5–15 data scientists and ML engineers, including hiring, career development, code review, and hands-on technical leadership on critical projects
• Partner with product, engineering, and business leadership to scope data science opportunities, translate requirements into modeling work, and communicate results to non-technical stakeholders
• Lead technical design and architecture decisions for ML systems—feature engineering, model selection, validation, deployment—balancing rigor with time-to-value
• Establish team standards: code quality, experimentation discipline, documentation, and reproducibility across the data science function
• Drive adoption of new tools, frameworks, or methodologies (e.g., LLMs, causal inference, AutoML) where they unlock new capabilities or improve team efficiency
• Report progress and business impact to senior leadership; advocate for data science investment and headcount based on roadmap priorities

Candidate Requirements

• 8+ years of hands-on data science and machine learning work—model development, statistical analysis, experimentation, and production ML systems
• 3+ years of people leadership: building and managing a data science or ML team, mentoring junior analysts or engineers, and ownership of hiring and career development
• Strong technical depth in Python, SQL, and modern ML frameworks; experience shipping models to production and understanding deployment constraints
• Demonstrated ability to translate business problems into data science questions and communicate modeling results to executives and product teams
• Experience setting technical strategy and standards—code quality, experimentation rigor, documentation, tooling—and driving team adoption
• Comfort working remote and across distributed, cross-functional teams