Staff/Senior Machine Learning Scientist - Forecasting

Specific responsibilities include:

Own end-to-end ML systems: scoping, feature engineering, model development, backtesting/validation, deployment (with platform partners), monitoring/alerting, retraining cadence, and ongoing reliability improvements.

Create and maintain production-safe evaluation infrastructure: automated backtests, error decomposition, uncertainty quantification, data validation, regression gates, and auditable model/version lineage.

Build AI-assisted/agentic development workflows (e.g., Claude Code) to automate repetitive tasks with human review and measurable quality gates.

Define success metrics tied to business outcomes; communicate assumptions, limitations, and risk so model outputs are used correctly by stakeholders.

Write production-quality, testable code and support reproducible workflows.

Partner across functions to translate business needs into a prioritized technical roadmap and measurable impact.

Build and improve forecasts across time horizons and business segments (demand, inventory, supply chain, resource allocation), selecting approaches that balance accuracy, stability, interpretability, and operational cost.

Productize forecast outputs for stakeholders: clear definitions and assumptions, versioned releases, and reporting that explains what changed, why it changed, and how uncertainty should shape decisions.

Feature engineering, uncertainty quantification and calibration, hierarchical/segmented forecasting where appropriate.

Partner with operations, supply chain, inventory, finance, and marketing leaders.

Qualifications
5+ years in applied ML/data science, including owning models in production (deployment, monitoring, incident response, retraining)

Strong forecasting expertise (time-series methods, feature engineering, rigorous backtesting) OR deep expertise in Bayesian statistical methods and probabilistic programming

Strong statistics fundamentals; comfort with probabilistic forecasting and explaining uncertainty in practical terms

Strong Python (or R) and SQL; writes production-quality, testable code

Strong communication and cross-functional collaboration with non-technical stakeholders

Experience using AI-assisted development workflows responsibly (verification loops, reproducibility, automated checks)

Additional expectations — Staff level:

8+ years in applied ML/data science, or PhD with 3+ years of applied experience

Experience building ML systems end-to-end (not just models): backtesting frameworks, scheduled retraining, monitoring/alerting, and automated reporting into planning or decision workflows

Demonstrated ability to inherit complex systems built by others and make sound architectural decisions with high autonomy

Technical leadership: raises the bar on evaluation, reproducibility, and production practices; mentors less-senior team members