
Data Scientist, Forecasting
Gridware
full-time
Posted on:
Location Type: Hybrid
Location: San Francisco • California • United States
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Salary
💰 $180,000 - $200,000 per year
About the role
- Build and productionize forecasting models to predict weather-driven grid events, hazard incidence, and downstream operational workload.
- Develop demand forecasts for analyst staffing, incorporating weather signals, seasonality, geographic deployment footprint, and historical event patterns.
- Quantify relationships between environmental factors (e.g., wind, precipitation, temperature, vegetation conditions) and hazard/event types across service territories.
- Design end-to-end modeling pipelines, including data ingestion, feature engineering, model validation, deployment, and monitoring.
- Establish model performance tracking, drift detection, and retraining strategies to ensure reliability in dynamic conditions.
- Translate model outputs and uncertainty into clear, actionable recommendations for operational leadership.
Requirements
- 5+ years of experience building and deploying models into production environments
- Experience with forecasting models
- Strong programming skills with demonstrated proficiency in Python and SQL
- Experience working within modern software stacks, including cloud platforms, containerization, and CI/CD workflows
- Experience working cross-functionally with operations or business stakeholders
Benefits
- Health, Dental & Vision (Gold and Platinum with some providers plans fully covered)
- Paid parental leave
- Alternating day off (every other Monday)
- “Off the Grid”, a two week per year paid break for all employees.
- Commuter allowance
- Company-paid training
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
forecasting modelsPythonSQLdata ingestionfeature engineeringmodel validationmodel deploymentmodel monitoringdrift detectionretraining strategies
Soft Skills
cross-functional collaborationcommunicationanalytical thinkingproblem-solvingactionable recommendations