Staff Data Scientist: Architect Production ML

Hims; Greater London; On-site; GBP 150,000 - 190,000; Full time

Job summary

Hims & Hers is seeking a Staff Data Scientist to serve as a technical anchor for the data organization, guiding production-ready ML efforts from strategy to implementation. You will balance pragmatic architecture with rapid delivery, ensure scalability, and drive measurable business impact.

You will bridge business goals with engineering execution, owning end-to-end ML product deployment and setting high standards for model development across a cloud-based stack (AWS/GCP).

Qualifications

• 8+ years of experience in Data Science or ML Engineering.
• 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).
• Experience with CI/CD, ML Ops, and managing the full lifecycle of models in a cloud-based production environment (AWS or GCP).
• BS, MS, or PhD in a quantitative field or equivalent field expertise.

Responsibilities

• Architect automated ML systems and core ML infrastructure, balancing speed and quality.
• Translate ambiguous business questions into concrete technical roadmaps with measurable results.
• Lead end-to-end deployment of ML products, ensuring robustness and production readiness.
• Collaborate with Engineering, Product, and Business to align technical work with business goals.
• Define technical standards and lead design reviews for reproducibility and integration.
• Own the full model lifecycle from data design to long-term performance and value.
• Mentor Senior and Mid-level Data Scientists to raise the team's technical bar.

Skills

Python

SQL

Pandas

NumPy

Scikit-Learn

PyTorch

XGBoost/LightGBM

CI/CD

ML Ops

AWS

GCP

Leadership

Problem Solving

Education

BS/MS/PhD in quantitative field

Tools

AWS

GCP

Job description

Hims & Hers is seeking a Staff Data Scientist to serve as a technical anchor for the data organization, guiding production-ready ML efforts from strategy to implementation. You will balance pragmatic architecture with rapid delivery, ensure scalability, and drive measurable business impact.

You will bridge business goals with engineering execution, owning end-to-end ML product deployment and setting high standards for model development across a cloud-based stack (AWS/GCP).