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Hims & Hers, a leading health and wellness platform and NYSE-traded public company, is seeking a Staff Data Scientist to serve as a technical leader and force multiplier within the data organization. This role bridges business strategy and production-ready machine learning, working across customer acquisition, supply chain optimization, and marketing attribution.
Key Responsibilities:
- Architect and lead the design and implementation of automated ML systems, balancing pragmatic choices between building vs. buying and simple vs. complex solutions while writing production code and establishing core ML infrastructure
- Translate ambiguous business questions into concrete technical roadmaps that deliver actionable results
- Lead end-to-end deployment of ML products, ensuring accuracy, robustness, maintainability, and full integration into production infrastructure
- Partner across Engineering, Product, and Business teams to align technical strategy with business objectives and core metrics
- Establish and enforce technical standards for model development through design docs and peer reviews, ensuring reproducibility and integration with Data and Analytics Engineering partners
- Own the complete model lifecycle from initial data design through long-term performance monitoring and business value delivery
- Mentor Senior and Mid-level Data Scientists, elevating technical standards and fostering continuous learning across the data organization
Requirements:
- 8+ years of experience in Data Science or ML Engineering with proven track record of building production systems delivering measurable business impact
- High proficiency in Python and SQL; expert-level experience with Python data stack (pandas, NumPy, scikit-learn) and at least one major ML framework (PyTorch, XGBoost, or LightGBM)
- Ability to work on unique, conceptually complex problems requiring broad impact; experience building for long-term scalability while delivering immediate value
- Proven ability to influence without authority and translate complex technical logic into compelling narratives for executive leadership
- Experience with CI/CD, ML Ops, and managing full model lifecycle in cloud-based production environments (AWS or GCP)
- BS, MS, or PhD in quantitative field (Data Science, Statistics, Economics, CS, Applied Math, etc.) or equivalent field expertise
Preferred Qualifications:
- Experience taking first machine learning models in an organization from exploratory notebooks to reliable, automated production pipelines
- Advanced business ML experience in one or more of: customer behavior and propensity modeling (churn, propensity-to-buy, lead scoring, LTV); applied forecasting (time-series, anomaly detection, non-stationary data); optimization (marketing spend, inventory, resource allocation); or causal inference (quasi-experiments, difference-in-differences)
