Senior Staff Data Scientist - ML Systems & Strategy

hims-and-hers; United States; Remote; USD 180,000 - 280,000; Full time

Benefits offered by this job

Health coverage

Parental leave

Retirement matching

Employee stock purchase program

Unlimited PTO

Team retreats

Job summary

Hims & Hers is seeking a senior ML leader to turn ambiguous business needs into production ML systems. You’ll establish ML infrastructure, guide data/engineering partners, and own models throughout their lifecycle—from data design to long-term performance.

You’ll design automated systems, translate questions into roadmaps, deploy robust ML products, and set lifecycle standards while mentoring teams and delivering measurable business impact.

Qualifications

• Eight+ years in data science or ML engineering with production systems.
• Proficient in Python and SQL with data-science stacks (pandas, NumPy, scikit-learn).
• Experience moving ML from exploration to automated production pipelines.
• Demonstrated ability to lead across data, engineering, and business teams.
• Bachelor’s or higher in a quantitative field; strong communication skills.

Responsibilities

• Design and build automated ML systems and core infrastructure.
• Translate business questions into technical roadmaps and models.
• Lead deployment of robust ML products integrated with production systems.
• Set model development standards and ensure reproducibility.
• Track model performance and business value over time.
• Mentor data scientists and influence cross-functional teams.

Skills

Python

SQL

Pandas

NumPy

Scikit-learn

ML Frameworks

Executive communication

Education

Bachelor's or higher in a quantitative field

Tools

PyTorch

XGBoost

LightGBM

CI/CD

MLOps

AWS or GCP

Job description

Hims & Hers is seeking a senior ML leader to turn ambiguous business needs into production ML systems. You’ll establish ML infrastructure, guide data/engineering partners, and own models throughout their lifecycle—from data design to long-term performance.

You’ll design automated systems, translate questions into roadmaps, deploy robust ML products, and set lifecycle standards while mentoring teams and delivering measurable business impact.