Staff Data Scientist

Role overview

Serve as a senior technical leader who turns ambiguous business needs into production machine-learning systems with measurable impact. You’ll help establish foundational ML infrastructure, guide technical direction across data and engineering partners, and remain accountable for models throughout their lifecycle, from data design to long-term performance.

Responsibilities

- Design and build automated ML systems and core infrastructure, making pragmatic choices about architecture, tools, and implementation. - Translate business questions into technical roadmaps and models that support goals such as customer growth, supply planning, or marketing measurement. - Lead deployment of robust, maintainable ML products integrated with production systems. - Set model development standards through design documents, reviews, reproducibility practices, and collaboration with data and analytics engineering. - Track model performance and business value over time, and drive improvements across the model lifecycle. - Mentor data scientists and influence technical decisions across engineering, product, and business teams.

Requirements

- At least eight years of data science or ML engineering experience, including production systems that delivered measurable business results. - Strong Python and SQL skills, fluency with tools such as pandas, NumPy, and scikit-learn, and experience with a major ML framework such as PyTorch, XGBoost, or LightGBM. - Experience taking early-stage ML work from exploration to reliable, automated production pipelines. - Experience with CI/CD, MLOps, and model lifecycle management in a cloud environment such as AWS or GCP. - Ability to lead through influence, explain technical ideas to executive audiences, and balance scalable design with near-term value. - Degree in a quantitative field or equivalent expertise.

Nice to have

- Experience in one or more areas such as customer propensity or churn modeling, lifetime value, forecasting, anomaly detection, optimization, or causal inference and quasi-experimental measurement.

Benefits and work setup

- Full-time compensation includes salary and equity. - Benefits include health coverage, parental leave, retirement matching, and an employee stock purchase program. - Unlimited paid time off, company holidays, quarterly mental health days, and team retreats.