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Hims & Hers, a leading health and wellness platform and NYSE-traded public company, is seeking a Senior Data Scientist to drive technical execution and innovation within the data organization. You will translate complex business challenges into robust, scalable data products and machine learning models, operating with high autonomy from exploratory analysis through production deployment.
You will work closely with Product, Engineering, and Business stakeholders to deliver solutions that optimize operations, refine marketing efforts, and enhance customer experience. Key responsibilities include:
- Building from scratch in 0-to-1 environments, writing complex SQL, engineering features, and deploying baseline models quickly to prove value before iterating toward complex solutions.
- Owning the complete model lifecycle: data extraction, feature engineering, deployment, A/B testing, and ongoing performance monitoring.
- Partnering with Engineering, Product, and Finance teams to define technical requirements and translate model outputs into actionable business insights.
- Writing clean, modular, production-ready code and actively participating in peer code reviews.
- Navigating technical ambiguity, breaking down complex requirements into manageable milestones.
- Providing technical guidance and mentorship to junior data scientists and analysts.
The role emphasizes end-to-end execution, cross-functional collaboration, and upholding engineering rigor and technical standards.
REQUIREMENTS:
- 5+ years of applied experience in Data Science or ML Engineering, with a track record of delivering production-ready models that drive measurable business value.
- Strong expertise in Python and SQL; deep familiarity with the Python data stack (pandas, NumPy, scikit-learn) and standard ML frameworks (PyTorch, XGBoost, LightGBM).
- Proven ability to build for production; experience in cloud-based environments (AWS or GCP) and familiarity with CI/CD workflows, version control (Git), and ML Ops principles.
- Strong analytical problem-solving ability; ability to connect technical metrics to business outcomes and choose appropriate algorithms.
- Excellent communication skills for explaining technical concepts, model limitations, and findings to non-technical stakeholders.
- BS, MS, or equivalent experience in a quantitative field (Data Science, Statistics, Economics, CS, Applied Math, etc.).
PREFERRED QUALIFICATIONS:
- Experience taking the very first machine learning models in an organization from exploratory notebooks to reliable, automated production pipelines.
- Advanced Business ML experience in 1-2 of the following: Customer Behavior & Propensity Modeling (churn, propensity-to-buy, lead scoring, LTV); Applied Forecasting (time-series, anomaly detection, demand/revenue planning); Optimization (marketing spend, inventory, resource allocation); Causal Inference (robust experiments, quasi-experiments).
