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. That means real day-to-day collaboration across the Atlantic: shared standards, a shared roadmap, and enough overlap in working hours to pair on hard problems. You will work closely with the Director to figure out how to best organize a team split across two countries, from how work gets divided to how the team is structured, so that it delivers the most impact to the business rather than just replicating a US structure overseas. You will partner directly with Finance, Marketing, and Operations leadership to translate business priorities into a team roadmap, then make sure that roadmap actually ships.
You Will
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.
You Have
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.
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.
We are committed to building a workforce that reflects diverse perspectives and prioritizes ethics, wellness, and a strong sense of belonging. If you’re excited about this role, we encourage you to apply—even if you’re not sure if your background or experience is a perfect match.
Hims considers all qualified applicants for employment, including applicants with arrest or conviction records, in accordance with the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance, the California Fair Chance Act, and any similar state or local fair chance laws.
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