Description
As the Senior Manager, Data Science leading our UK team, you will be one of the earliest members of Hims & Hers' data science function in the UK.
You will help shape how the UK team works and grows from this early stage, without a long-established structure to inherit.
You are equal parts technical leader and people leader: you set the roadmap for what your UK team builds and in what order, unblock technical decisions when the path forward is unclear, and develop your reports into stronger technical leaders themselves.
You will report to the Director of Data Science (based in the US) and operate as one team with our US-based data scientists, not a separate satellite.
Responsibilities:
- 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.
Requirements:
- 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.
Preferred Qualifications:
- US/UK Collaboration, Specifically: Direct experience working across a US/UK (or similar transatlantic) split team, ideally within a company still establishing its presence in one of those regions.
- Advanced Business ML (experience leading or reviewing work in 1-2 of the following):
- Customer Behavior & Propensity Modeling: churn, propensity-to-buy, lead scoring, or lifetime value (LTV) models used to drive targeted marketing and product interventions.
- Applied Forecasting: time-series forecasting, anomaly detection, or demand/revenue planning under limited historical data.
- Optimization: marketing spend, inventory, or resource allocation engines.
- Marketing Measurement: marketing mix modeling, multi-touch attribution, or incrementality testing to measure media effectiveness and inform budget allocation across channels.
- Industry Experience: healthcare, DTC/subscription, or pharma background is a plus, not a requirement.