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Hims & Hers, a NYSE-traded health and wellness platform, is seeking a Staff Data Scientist to serve as a technical leader and force multiplier for 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 of automated ML systems, balancing pragmatism with technical rigor while writing production code and establishing core ML infrastructure
- Translate ambiguous business questions into concrete technical roadmaps with clear, actionable results
- Lead end-to-end deployment of ML products, ensuring accuracy, robustness, maintainability, and production integration
- Partner across Engineering, Product, and Business to align technical strategy with core business metrics
- Establish standards for model development through design docs and peer reviews, ensuring reproducibility and integration with Data and Analytics Engineering teams
- Own the full model lifecycle from 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
The ideal candidate will identify which problems are worth solving to move the needle for customers and simplify ambiguous challenges into executable paths for the team.
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, high-impact problems requiring conceptual thinking and systemic problem-solving
- 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 1-2 areas: Customer Behavior & 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)
