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.

• Build Scalable Solutions: Lead the design and delivery of ML systems and data products that directly impact company-wide growth and operational strategy

• Translate Business Needs: Turn ambiguous business questions (from customer acquisition to churn dynamics) into concrete technical roadmaps that deliver clear, actionable results

• Drive Execution and Reliability: Lead the end-to-end deployment of ML products, ensuring they are not just accurate but robust, maintainable, and fully integrated into our production infrastructure

• Connect Technical & Business Goals: Partner across Engineering, Product, and Finance to ensure our technical strategy is solving the right business problems and moving our core metrics

• Define Technical Standards: Act as a force multiplier by establishing the standards for model development. You will lead design docs and peer reviews that ensure our work is reproducible and integrates seamlessly with the work of our Data and Analytics Engineering partners

• Own the Results: Take accountability for the full model lifecycle, from the initial data design through to the long-term performance and business value of production systems

• 8+ years of experience in Data Science or ML Engineering, with a proven track record of building production systems that deliver measurable business impact

• Technical Mastery: High proficiency in Python and SQL. Expert-level experience with the Python data stack (pandas, NumPy, scikit-learn) and at least one major ML framework (such as PyTorch or XGBoost/LightGBM)

• Systemic Problem Solving: Ability to work on unique issues requiring conceptual thinking and broad impact. You know how to build for long-term scalability while delivering immediate value

• Leadership & Influence: Proven ability to influence without authority. You can translate complex technical logic into compelling narratives for executive leadership

• Engineering Rigor: Experience with CI/CD, ML Ops, and managing the full lifecycle of models in a cloud-based production environment (AWS or GCP)

• Education: BS, MS, or PhD in a quantitative field (Data Science, Statistics, Economics, CS, Applied Math, etc.) or equivalent field expertise

• Forecasting Expertise: Specialization in time-series analysis, probabilistic forecasting, and handling non-stationary data in high-growth environments

• Optimization & Operations Research: Experience building optimization engines for marketing spend, inventory management, or resource allocation

• Causal Inference: Expertise in experimental design beyond standard A/B testing, including quasi-experiments and structural equation modeling