Note: The job is a remote job and is open to candidates in USA. Hims & Hers is a health and wellness platform focused on providing personalized, accessible healthcare. The Senior Data Scientist will develop scalable data products and machine learning models, owning projects from exploratory analysis through production deployment while partnering with Product, Engineering, Finance, and Business stakeholders. The role also involves maintaining technical standards, delivering actionable insights, and mentoring junior team members.
Responsibilities
- Build from Scratch: Thrive in a 0-to-1 environment. You are comfortable rolling up your sleeves to write complex SQL, engineer your own features, and deploy baseline models (heuristics or simple ML) quickly to prove value before iterating toward complex solutions
- End-to-End Execution: Own the complete model lifecycle, from data extraction and feature engineering to deployment, A/B testing, and ongoing performance monitoring
- Cross-Functional Collaboration: Partner closely with Engineering, Product, and Finance teams to define technical requirements and translate model outputs into clear, actionable business insights
- Uphold Technical Standards: Write clean, modular, and production-ready code. Actively participate in peer code reviews and contribute to the team’s technical best practices
- Drive Project Delivery: Navigate technical ambiguity within your domain, breaking down complex project requirements into manageable, executable milestones
- Team Mentorship: Provide technical guidance and support to junior data scientists and analysts, helping them troubleshoot roadblocks and adopt best practices
Skills
- • 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
- • Technical Proficiency: Strong expertise in Python and SQL. Deep familiarity with the Python data stack (pandas, NumPy, scikit-learn) and standard ML frameworks (such as PyTorch, XGBoost, or LightGBM)
- • Engineering Rigor: Proven ability to build for production. Experience working in cloud-based environments (AWS or GCP) and familiarity with CI/CD workflows, version control (Git), and ML Ops principles
- • Analytical Problem Solving: Strong ability to connect technical metrics to business outcomes. You know how to choose the right algorithm for the right problem rather than just the most complex one
- • Communication: Excellent ability to explain technical concepts, model limitations, and analytical findings to non-technical stakeholders
- • Education: BS, MS, or equivalent experience in a quantitative field (Data Science, Statistics, Economics, CS, Applied Math, etc.)
- • 0-to-1 Execution: Experience taking the very first machine learning models in an organization from exploratory notebooks to reliable, automated production pipelines
- + 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-
Benefits
- Equity compensation for full-time roles
- Unlimited PTO
- Company holidays
- Quarterly mental health days
- Comprehensive health benefits including medical, dental & vision
- Parental leave
- Employee Stock Purchase Program (ESPP)
- 401k benefits with employer matching contribution
- Offsite team retreats
- Talent-first flexible/remote work approach
Company Overview
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