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Sr. Manager, Data Science

Hims And Hers UK RemoteEUR 100,000–130,000 / yearSenior

Key requirements

  • Python
  • Sql
  • Ci/Cd
  • Data Science
Hims & Hers is the leading health and wellness platform, on a mission to help the world feel great through the power of better health. We are redefining healthcare by putting the customer first and delivering access to care that is affordable, accessible, and personal, from diagnosis to treatment to delivery. No two people are the same, so we provide access to personalized care designed for results. By normalizing health & wellness challenges and innovating on their solutions, we’re making better health outcomes easier to achieve.  Hims & Hers is a public company, traded on the NYSE under the ticker symbol “HIMS.” To learn more about the brand and offerings, you can visit hims.com/about and hims.com/how-it-works . For information on the company’s outstanding benefits, culture, and its talent-first flexible/remote work approach, see below and visit www.hims.com/careers-professionals . About the Role: 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, joining as we stand up the team alongside the company's new technology hub there. 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. 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 w

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