Design, build, and maintain forecasting and optimization models embedded in core product workflows
Model behaviors, impacts of weather, and behind the meter resources
Translate operational and business requirements into quantitative frameworks and production systems
Own the full lifecycle of quantitative systems — from data ingestion and feature development through deployment, monitoring, and reliability in production environments
Design and maintain data pipelines, schemas, and interfaces that support production forecasting and optimization workflows
Establish best practices for model development, validation, performance monitoring, and operational robustness
Partner closely with engineering to define interfaces, data contracts, and system boundaries that enable robust quantitative services
Provide technical leadership and guidance on quantitative methods across the organization

7+ years of experience in data science, applied statistics, quantitative engineering, or related fields
Strong understanding of constraint programming and multi-objective optimization for real-time systems
Background in time-series analysis and regression-based modeling
Background in Bayesian modeling
Experience building optimization models in the electricity space and implementing them in the US energy / capacity markets
Proven experience deploying models into production environments
Comfort working with highly-variable, real-world data sets from numerous and varying sources and building supporting infrastructure
Strong programming skills in Python (or similar)
Ability to operate autonomously and make sound technical trade-offs
Strong communication skills across technical and non-technical audiences.

Data Scientist

Key skills