Sr Analyst, AI Analytics

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 Sr Analyst, AI Analytics, you will serve as a trusted analytics partner to the teams building and operating Hims & Hers' AI products (e.g., patient-facing AI chat assistants, internal AI tools for care teams). You will work cross-functionally with engineers, product managers, clinical operations, and finance to measure product performance, quantify commercial and clinical impact, and design rigorous experiments and measurement plans for AI-driven experiences. You will operate with high autonomy on your projects and report to the Sr Manager of AI Analytics.

• Measure and Recommend: Deliver data-driven insight on AI product performance, with clear recommendations that inform Product roadmap and investment decisions

• Run Experiments: Design and analyze tests for AI product launches and iterations (e.g., GrowthBook-based tests), including success metrics, sample-size estimation, guardrails, and results interpretation

• Apply Causal Methods: Use quasi-experimental methods, including difference-in-differences, fixed-effects models, and propensity score matching, to assess AI impact on retention and clinical/operational outcomes when randomized tests aren't feasible

• Build Measurement Plans: Define and validate metrics and measurement plans for new AI product use cases, from pre-launch instrumentation through post-launch readouts, translating ambiguous business questions into well-instrumented metrics within a complex, multi-source data environment

• Connect Model to Business: Partner with ML/Applied AI teams to evaluate model performance in production and translate model-level signals (confidence, escalation type, resolution outcome) into product and business metrics

• Quantify Commercial Impact: Analyze cost savings, handle-time efficiency, and support deflection for AI products, and size retention and churn addressability for AI initiatives

• Ensure Data Integrity: Work cross-functionally with AI/ML Engineering, Analytics Engineering, and Data Engineering teams to define tracking requirements and ensure data integrity across AI product pipelines and event schemas

• Go Beyond Dashboards: Use Python or R alongside SQL to develop analytical solutions (e.g., observational studies, survival/retention modeling) that go beyond what dashboards alone can answer

• Enable Self-Service: Design intuitive Looker dashboards that make AI product metrics accessible to Product and Care Operations teams

• Analyze Performance: Run deep-dive analyses, including user and session segmentation, to answer questions about AI product performance, identify where AI products are (and aren't) working, quantify opportunity areas, and recommend clear actions

• 5+ years of experience in analytics, data science, or a related quantitative field

• Education: Bachelor's degree in Statistics, Mathematics, Economics, Computer Science, Engineering, or a related field

• Experimentation: 3+ years of experience designing, executing, and analyzing A/B tests, ideally including tests on AI/ML-driven product experiences

• Data Partnership: Experience working with data engineering and analytics engineering teams, including dbt-modeled production pipelines

• Statistics: Solid grounding in experimental design and statistical inference, with exposure to causal inference techniques beyond standard A/B testing, such as quasi-experimental methods or observational study design (e.g., fixed-effects regression, survival analysis)

• Subscription Metrics: Experience with subscription-based business models and metrics, including cohort analysis, retention/churn analysis, and addressability sizing

• Technical Proficiency: Strong SQL skills (BigQuery preferred), working knowledge of Python or R, and advanced experience with Looker or Tableau

• Communication: Ability to explain technical analyses clearly to non-technical audiences, including senior leadership

• Influence: Experience turning analysis into recommendations that change product decisions

• Ownership: A proactive, ownership-driven mindset with strong organizational and prioritization skills and comfort with ambiguity

• Experience evaluating conversational AI or LLM-based products (intent classification, containment/escalation, hallucination or quality monitoring)

• Familiarity with telehealth concepts or subscription-based healthcare models, including clinical safety considerations in AI-assisted workflows

• Experience with GrowthBook or a comparable experimentation platform

• Experience with survival analysis or time-to-event modeling (e.g., lifelines, pyfixest, statsmodels)

• dbt, Airflow, and Databricks experience