Staff Data Scientist

Hims & Hers•,

22h•$190,000 - $230,000

About The Position

As a Staff Data Scientist at Hims & Hers, you will be a technical leader and force multiplier for the data organization. You will identify high-impact problems, simplify ambiguous challenges into executable plans, and connect business strategy with production-ready machine learning. You may work on areas such as growth, supply chain optimization, and marketing attribution, ensuring data products are technically sound, scalable, and deliver measurable business results. You will lead the design and implementation of automated machine learning systems, make pragmatic architectural decisions, and contribute production code and core ML infrastructure. The role involves turning business needs into technical roadmaps; leading reliable end-to-end ML deployment; partnering with Engineering, Product, and Business; defining model-development standards; and owning model performance and business value throughout the lifecycle. You will also mentor Senior and Mid-level Data Scientists and foster continuous learning.

Requirements

At least 8 years of experience in Data Science or ML Engineering, with a track record of building production systems that deliver measurable business impact.
High proficiency in Python and SQL.
Expert-level experience with the Python data stack, including pandas, NumPy, and scikit-learn.
Experience with at least one major ML framework, such as PyTorch, XGBoost, or LightGBM.
Ability to solve unique, broad-impact problems through conceptual thinking, building for scalability while delivering immediate value.
Proven ability to influence without authority and explain complex technical concepts persuasively to executive leadership.
Experience with CI/CD, MLOps, and managing the full model lifecycle in a cloud-based production environment using AWS or GCP.
BS, MS, or PhD in a quantitative field such as Data Science, Statistics, Economics, Computer Science, or Applied Math, or equivalent field expertise.

Nice To Haves

Experience taking an organization's first machine learning models from exploratory notebooks to reliable, automated production pipelines.
Experience in one or two of the following areas: customer behavior and propensity modeling for churn, propensity to buy, lead scoring, or lifetime value; applied forecasting, anomaly detection, or non-stationary data for demand or revenue planning; optimization for marketing spend, inventory, or resource allocation; or causal inference using robust experiments such as quasi-experiments and difference-in-differences.

Responsibilities

Lead the design and implementation of automated machine learning systems and core ML infrastructure, balancing quality, speed, and pragmatic architectural choices.
Translate ambiguous business questions into concrete technical roadmaps and actionable results.
Lead end-to-end deployment of ML products, ensuring they are robust, maintainable, and integrated into production infrastructure.
Partner with Engineering, Product, and Business to align technical strategy with business goals and core metrics.
Establish model-development standards and lead design documentation and peer reviews to support reproducible work and integration with Data and Analytics Engineering.
Own the full model lifecycle, from data design through long-term production performance and business value.
Mentor Senior and Mid-level Data Scientists and foster continuous learning across the data organization.