Staff Analyst, AI Product

We need to output HTML fragment with sections only if real content. Use

for headings: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We have content: About The Role (description). Then responsibilities list (the bullet points under "You Will"). Then qualifications: "You Have" list (requirements). Then "Preferred Qualifications". Then Benefits list. No explicit Pay or Schedule mentioned, so omit those sections. Also we should not repeat job title. Also we should not include metadata like Job ID. Also we should remove boilerplate: mission statement at start? That's about company, not role specifics. Should we include that? The instruction: Preserve ALL substantive information; do NOT invent or summarize away detail. Remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions. The mission statement at top is generic company description; is that substantive? It's about the company, not the role. Might be considered boilerplate? It's not EEO etc. It's about the company mission. Could be considered part of job posting but not role-specific. The instruction: "Preserve ALL substantive information; do NOT invent or summarize away detail." The mission statement is substantive about the company, but maybe we can keep as a paragraph before sections? There's no heading for company overview. We could include a

with that mission statement. However they said use

for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). They didn't forbid other headings, but they said use those for section headings. Probably we should include a brief intro paragraph before sections. That's fine. We need to format responsibilities as

• . Qualifications as
  • . Preferred qualifications maybe as another
    • under a heading? Could be part of Qualifications or separate "Preferred Qualifications". Since they list "Preferred Qualifications" heading, we could make a section with heading "Preferred Qualifications". That's allowed as it's not in the list but we could treat as part of Qualifications? The instruction: Use

      for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). It says e.g., not exclusive. So we can use other headings if needed, but better to stick to those. Preferred Qualifications could be under Qualifications heading with a note? But we have both required and preferred. Could combine under Qualifications with two sublists? But they said use

      • for any list of duties/requirements/benefits. So we can have a

        Qualifications

        and inside have two
        maybe with a

        indicating required vs preferred. Or we could have separate headings: Requirements and Qualifications. They listed both Requirements and Qualifications as possible headings. So we could use Requirements for required qualifications, and Qualifications for preferred? Or vice versa. Usually Requirements = required, Qualifications = preferred. We'll do that. Thus sections: About the role (

        About the role

        with paragraph). Responsibilities (

        Responsibilities

        with ul). Requirements (

        Requirements

        with ul). Qualifications (

        Qualifications

        with ul for preferred). Benefits (

        Benefits

        with ul). No Pay or Schedule. We need to preserve all substantive information: include the details about reporting to Manager of AI Analytics, etc. Also include the link to learn more about brand etc? That's maybe boilerplate? It's about the company; could keep as part of intro paragraph. The sentences: "Hims & Hers is the leading health and wellness platform..." etc. Also "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." That's somewhat generic but could be kept as intro. We should remove EEO etc at bottom. Also remove accommodation contact etc. Also remove privacy statement etc. Let's craft. Intro paragraph: combine the first paragraph about mission and maybe the "To learn more..." sentence. Then About the role section: include the paragraph starting "About The Role As a Staff Analyst, AI, you will serve..." etc. Responsibilities: list each bullet from "You Will". Need to capture each line. Let's extract responsibilities bullet points: - Provide thought leadership and strategic partnership to AI Product stakeholders, influencing roadmap and investment decisions with data-driven insight into AI product performance - Own experimentation strategy for AI product launches and iterations (e.g., GrowthBook-based tests), including test design, success metrics, sample-size estimation, guardrails, and results interpretation - Apply causal inference and quasi-experimental methods — including fixed-effects models, difference-in-differences, and propensity score matching — to assess AI impact on retention and clinical/operational outcomes when randomized tests aren't feasible - Define, validate, and own metrics frameworks and measurement plans for new and evolving AI product use cases — from pre-launch instrumentation through post-launch readouts — translating ambiguous business questions into rigorous, well-instrumented metrics within a complex, multi-source data environment - 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 - Own commercial impact analysis for AI products — cost savings, handle-time efficiency, and support deflection — and quantify retention/churn addressability to size the impact of AI initiatives - 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 - Use Python or R alongside SQL to develop scalable analytical solutions (e.g., observational studies, survival/retention modeling) that go beyond what dashboards alone can answer - Design intuitive Looker dashboards that democratize AI product metrics and elevate self-service across Product and Care Operations teams - Conduct deep-dive analyses — including user/session segmentation — to answer strategic questions about AI product performance, identify where AI products are (and aren't) working, quantify opportunity areas, and recommend clear actions - Champion data quality, documentation, and deliverable consistency across dbt models and BigQuery tables that power AI metrics, while mentoring junior analysts Now Requirements (You Have): - 8+ years of experience in analytics, data science, or a related quantitative field - Bachelor's degree in Statistics, Mathematics, Economics, Computer Science, Engineering, or a related field - 5+ years of experience designing, executing, and analyzing A/B tests, ideally including tests on AI/ML-driven product experiences - Experience partnering with ML teams to evaluate model outputs and translate them into product/business metrics (e.g., classification accuracy, confidence scores, resolution quality) - Extensive experience working with data engineering and analytics engineering teams, including dbt-modeled production pipelines - Strong knowledge of experimental design and statistical inference; experience applying causal inference techniques beyond standard A/B testing, such as quasi-experimental methods or observational study design (e.g., fixed-effects regression, survival analysis) - Experience with subscription-based business models and metrics, including cohort analysis, retention/churn analysis, and addressability sizing - Expert SQL skills (BigQuery preferred) and advanced experience with Looker or Tableau - Ability to explain technical analyses clearly to non-technical audiences, including senior leadership - Experience shaping and influencing product strategy, not just executing analyses - A proactive, ownership-driven mindset with strong organizational and prioritization skills Preferred Qualifications: - 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 Benefits list: - Competitive salary & equity compensation for full-time roles - Unlimited PTO, company holidays, and quarterly mental health days - Comprehensive health benefits including medical, dental & vision, and parental leave - Employee Stock Purchase Program (ESPP) - 401k benefits with employer matching contribution - Offsite team retreats We should also maybe include the line about "We are committed to building a workforce that reflects diverse perspectives..." but that's EEO/diversity statement; might be considered boilerplate? It's not EEO legal but it's a diversity statement. The instruction says remove EEO/equal-opportunity/scam-warning boilerplate. This is a diversity commitment; could be considered EEO boilerplate. Safer to remove. Also remove the lines about Fair Chance Ordinance, Massachusetts lie detector test, accommodations, privacy statement, etc. Now produce HTML. We'll wrap sections in appropriate tags. Let's craft:

        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.

        About the role

        As a Staff Analyst, AI, you will serve as a strategic 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 shape the experimentation and measurement strategy for AI-driven experiences. This position will report to the Manager of AI Analytics.

        Responsibilities

        • Provide thought leadership and strategic partnership to AI Product stakeholders, influencing roadmap and investment decisions with data-driven insight into AI product performance
        • Own experimentation strategy for AI product launches and iterations (e.g., GrowthBook-based tests), including test design, success metrics, sample-size estimation, guardrails, and results interpretation
        • Apply causal inference and quasi-experimental methods — including fixed-effects models, difference-in-differences, and propensity score matching — to assess AI impact on retention and clinical/operational outcomes when randomized tests aren't feasible
        • Define, validate, and own metrics frameworks and measurement plans for new and evolving AI product use cases — from pre-launch instrumentation through post-launch readouts — translating ambiguous business questions into rigorous, well-instrumented metrics within a complex, multi-source data environment
        • 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
        • Own commercial impact analysis for AI products — cost savings, handle-time efficiency, and support deflection — and quantify retention/churn addressability to size the impact of AI initiatives
        • 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
        • Use Python or R alongside SQL to develop scalable analytical solutions (e.g., observational studies, survival/retention modeling) that go beyond what dashboards alone can answer
        • Design