Principal Energy Storage Software Optimization Engineer
New
USAFull-TimePrincipal
Salary not disclosed
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Job Details
- Experience
- 8-10+ yrs
- Required Skills
- PostgreSQLPythonSQLGitMachine LearningNumpyNosqlPandasRESTful APIsGitHubDeep Learning
Requirements
- 8-10+ yrs of optimization based python programming, mixed-integer linear programming (MILP), stochastic optimization, & predictive modeling experience
- Machine learning development experience in production ready coding environments focused on complex projects
- Well versed in Python-based optimization toolkits such as Pyomo, CVXPY GurobiPy, etc.
- Expert in Python stack - scipy, numpy, pandas, etc...
- Experience working in APIs databases like SQL, NoSQL, and RESTful to a process and manipulate large datasets
- Expertise in the Amazon Web Services (AWS) Sagemaker Machine Learning platform
- Solid understanding convex optimization techniques (Linear/Mixed Integer programming) and time-series forecasting (PostgreSQL, TimescaleDB, InfluxDB)
- Well versed in Bitbucket, git, or GitHub
- Well versed in machine learning concepts such as classification, deep learning, deep neural networks (DNN), reinforcement learning, and regression problem solving techniques
Responsibilities
- Develops and implements quantitative predictive models for utility-scale renewables projects operating in wholesale electricity markets with a key focus on energy storage initiatives
- Develops, updates, and implements mixed-integer linear programming (MILP) optimization models for energy storage, asset management, and energy trading initiatives
- Creates, designs, & test multitasking time series forecast models in AWS Sagemakeer machine learning environment
- Utilize forward thinking techniques such as optimal control, deep learning, machine learning (AI/ML), and reinforcement learning to evaluate and update current protocols
- Drive the implementation of full-lifecycle ML/AI solutions and take ownership of real-time troubleshooting
- Optimization modeling to forecast congestion, assess congestion drivers, and assist in locational marginal pricing (LMP) assessments
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