Staff Data Scientist

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.

As a Staff Data Scientist at Hims & Hers, you are a technical leader and a "force multiplier" for our data organization. You do not just solve the most difficult problems; you identify which problems are worth solving to move the needle for our customers. You will serve as a technical anchor, simplifying ambiguous problems into executable paths for the team.

In this role, you will bridge the gap between business strategy and production-ready machine learning. Whether you are building frameworks for growth, optimizing our supply chain, or refining marketing attribution, you will ensure our data products are technically sound, scalable, and built to deliver measurable business results.

• Customer Behavior & Propensity Modeling: Building predictive models for churn, propensity-to-buy, lead scoring, or lifetime value (LTV) to directly drive targeted marketing and product interventions.

• Applied Forecasting: Time-series forecasting, anomaly detection, or handling non-stationary data for demand or revenue planning.

• Optimization: Building engines for marketing spend, inventory management, or resource allocation.

• Causal Inference: Designing robust experiments (e.g., quasi-experiments, difference-in-differences) to measure true business impact beyond standard A/B testing.