Senior Data Scientist
Community Energy Labs
Posted
Jul 31, 2026
Location
Remote (US)
Type
Full-time
Compensation
$155000 - $185000
Mission
What you will drive
- Lead cross-functional teams to define, build, validate, and deploy advanced predictive analytics solutions that drive measurable business outcomes.
- Translate business objectives and stakeholder needs into clear analytical requirements and implementation plans.
- Design and deploy constrained optimization systems (MPC, LP, MILP) and forecasting systems for real-world building energy management.
- Build scalable, robust ML architectures and MLOps pipelines, and collaborate with engineering to integrate models into production.
Impact
The difference you'll make
This role directly contributes to reducing carbon emissions and energy costs in schools and community buildings by optimizing energy use through AI-powered control systems, helping communities save money and the planet.
Profile
What makes you a great fit
- Advanced degree or equivalent experience in Applied Mathematics, Physics, Mechanical Engineering, Electrical Engineering, Controls, Operations Research, Computer Science, or related quantitative field.
- 5+ years of experience building production-grade models, optimization systems, forecasting systems, or control systems.
- 5+ years with Docker and Kubernetes, data engineering, and data pipelines.
- 5+ years with Python and scientific computing tools (NumPy, pandas, SciPy, TensorFlow, PyTorch), SQL, and Unix/Linux scripting.
- 3+ years in constrained optimization (LP, MILP, MPC), ML/time-series forecasting, control systems, or applied mathematics.
Benefits
What's in it for you
Base salary range: $155,000-$185,000, with milestone-based incentive compensation tied to agreed product, deployment, and platform outcomes. Remote position with occasional travel. Standard work hours Monday-Friday, 8:30am-5:30pm Pacific time.
About
Inside Community Energy Labs
Community Energy Labs (CEL) develops AI-powered control systems that help buildings interact with the grid and use energy when it's clean, cheap, and abundant—and less when it's not. They focus on schools and community buildings, which make up nearly 30% of U.S. commercial floorspace, and have been recognized by the U.S. Department of Energy, NSF, USDA, and California Energy Commission.