Suggested rewrite: Led a cross-functional initiative that improved [business outcome] by [measurable result], demonstrating experience relevant to this role...
Staff Data Scientist
- Remote from
- USA
- Salary
- USD 190k–230k / yr
- Department
- Data Science & Analytics
- Employment
- Full Time
- Experience
- Director
- Published
- Apply before
- 6 Dec 2026
- Listing views
- 17
- Application actions
- 0
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About this role.
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 Data Scientist at Hims & Hers, you are a technical leader and a “force multiplier” for our data organization. You do not just solve the most difficult problems; you identify which problems are worth solving to move the needle for our customers. You will serve as a technical anchor, simplifying ambiguous problems into executable paths for the team.
In this role, you will bridge the gap between business strategy and production-ready machine learning. Whether you are building frameworks for growth, optimizing our supply chain, or refining marketing attribution, you will ensure our data products are technically sound, scalable, and built to deliver measurable business results.
You Will:
Architect the 0-to-1 Foundation: Lead the design and implementation of automated ML systems. You know how to balance “doing it right” with “doing it fast,” making pragmatic architectural choices (build vs. buy, simple vs. complex) while rolling up your sleeves to write production code and establish our core ML infrastructure.
Translate Business Needs: Turn ambiguous business questions (from customer acquisition to churn dynamics) into concrete technical roadmaps that deliver clear, actionable results
Drive Execution and Reliability: Lead the end-to-end deployment of ML products, ensuring they are not just accurate but robust, maintainable, and fully integrated into our production infrastructure
Connect Technical & Business Goals: Partner across Engineering, Product, and Business to ensure our technical strategy is solving the right business problems and moving our core metrics
Define Technical Standards: Act as a force multiplier by establishing the standards for model development. You will lead design docs and peer reviews that ensure our work is reproducible and integrates with the work of our Data and Analytics Engineering partners
Own the Results: Take accountability for the full model lifecycle, from the initial data design through to the long-term performance and business value of production systems
Mentorship & Coaching: Actively mentor Senior and Mid-level Data Scientists, elevating the technical bar and fostering a culture of continuous learning across the data organization.
Experience & Skills:
8+ years of experience in Data Science or ML Engineering, with a proven track record of building production systems that deliver measurable business impact
Technical Mastery: High proficiency in Python and SQL. Expert-level experience with the Python data stack (pandas, NumPy, scikit-learn) and at least one major ML framework (such as PyTorch or XGBoost/LightGBM)
Systemic Problem Solving: Ability to work on unique issues requiring conceptual thinking and broad impact. You know how to build for long-term scalability while delivering immediate value
Leadership & Influence: Proven ability to influence without authority. You can translate complex technical logic into compelling narratives for executive leadership
Engineering Rigor: Experience with CI/CD, ML Ops, and managing the full lifecycle of models in a cloud-based production environment (AWS or GCP)
Education: BS, MS, or PhD in a quantitative field (Data Science, Statistics, Economics, CS, Applied Math, etc.) or equivalent field expertise
Preferred Qualifications:
0-to-1 Execution: 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: 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-in-differences) to measure true business impact beyond standard A/B testing.
Our Benefits (there are more but here are some highlights):
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 are committed to building a workforce that reflects diverse perspectives and prioritizes ethics, wellness, and a strong sense of belonging. If you’re excited about this role, we encourage you to apply—even if you’re not sure if your background or experience is a perfect match.
Hims considers all qualified applicants for employment, including applicants with arrest or conviction records, in accordance with the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance, the California Fair Chance Act, and any similar state or local fair chance laws.
It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.
Hims & Hers is committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If you need assistance or an accommodation due to a disability, please contact us at accommodations@forhims.com and describe the needed accommodation. Your privacy is important to us, and any information you share will only be used for the legitimate purpose of considering your request for accommodation. Hims & Hers gives consideration to all qualified applicants without regard to any protected status, including disability. Please do not send resumes to this email address.
To learn more about how we collect, use, retain, and disclose Personal Information, please visit our Global Candidate Privacy Statement.
This job listing has been manually reviewed by the Jobicy Trust & Safety Team for compliance with our posting guidelines, including verification of the company's legitimacy, accuracy of job details, clarity of remote work policy, and absence of misleading or fraudulent content.
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Sample interview questions
I would explain the business objective, data validation approach, feature and model choices, deployment architecture, monitoring metrics, and the measurable outcome. I would also highlight how I established ownership, retraining processes, and safeguards for model reliability.
I begin by defining the decision to improve, its expected economic or customer impact, and a baseline process. I then assess data quality, feasibility, time to value, and whether a simpler analytical or rules-based solution can achieve the objective before investing in ML.
I would introduce versioned data and code, reproducible training pipelines, automated testing, model registry and deployment workflows, monitoring, and clear documentation. I would phase the platform around the highest-value use cases to balance standardization with delivery speed.
I focus on the decision, expected business impact, uncertainty, and recommended action rather than model mechanics. I use clear visuals, describe assumptions and risks plainly, and connect technical metrics to customer, revenue, retention, or operational outcomes.
I would use randomized holdouts when feasible and compare treatment outcomes against a valid control group. Where experiments are not possible, I would consider carefully designed quasi-experimental approaches, such as difference-in-differences, while checking for selection bias and confounding.
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