Senior Manager of Data Science, Hims & Hers

• 8+ years of experience in Data Science or ML Engineering, including 2+ years directly managing data scientists or ML engineers
• Early-Stage Team Experience: Track record of joining or helping shape a brand-new team, office, or region from an early stage, rather than only working within an established structure
• Distributed/International Collaboration: Experience working on or leading a team split across countries or significant time zones, comfortable with the communication overhead and async workflows that requires
• Proven People Leadership: Track record of developing and retaining data science talent, including calibrating performance and managing through periods of team growth
• Technical Credibility: Strong enough in Python, SQL, and applied ML (regression/classification, time-series, causal methods) to review your team's technical decisions and unblock them, even without writing production code daily
• Prioritization Under Constraint: Demonstrated ability to help sequence a roadmap across competing stakeholder demands with limited headcount, and to make a clear, well-reasoned case for tradeoffs even when the final call is shared with others
• Executive Communication: Comfortable translating technical work into a narrative for senior leadership and non-technical stakeholders, and negotiating scope and timeline with them directly
• Engineering Rigor: Familiarity with production ML systems (cloud environments, CI/CD, ML Ops) sufficient to hold your team to a real production bar, not just notebook-quality work
• Education: BS, MS, or PhD in a quantitative field (Statistics, Mathematics, Economics, Physics, Operations Research, etc.) or equivalent experience. We weigh hands-on empirical research experience, such as a thesis or lab-based program, over the specific field of study

Desirable

• Help Establish the UK Team: As one of the earliest members of the UK team, help shape the working norms, technical culture, and onboarding experience for the data scientists who join after you
• Lead as One Distributed Team: Operate the UK team as a single function with the US-based data science org, not a separate silo, keeping standards, context, and priorities consistent across the time zone gap
• Shape the Roadmap: Partner with the Director of Data Science and the US team to prioritize competing requests from Marketing, Operations, Product, and Finance into a shared roadmap, and help make the tradeoff calls for what the UK team builds and when
• Stay Technically Close: Review designs and code at a level of depth that lets you catch bad technical decisions early, even though you are not the one writing production code day to day
• Unblock Ambiguity: Step into the highest-ambiguity problems on the team's roadmap when your team needs a second set of hands or a tie-breaking technical call
• Set the Technical Bar: Establish and enforce the standards (code review, documentation, model monitoring, experimentation rigor) your team builds against, consistent with the US team and in partnership with Staff-level ICs
• Own Delivery: Take accountability for whether your team's 0-to-1 builds actually ship, work reliably in production, and deliver the business outcome they were built for