Energy Modeling & Optimization Engineer
Somerville, MA /
Business Development & Analytics – Analytics /
Form Energy is a small team of passionate people committed to helping the world transition to a 100% renewable energy electric grid with new technology. We are developing a new class of ultra low cost, long duration energy storage systems which will make renewable energy fully dispatchable year-round and scalable worldwide. The company’s core technology was invented at MIT and is supported by category leading investors including ENI Next, Breakthrough Energy Ventures, The Engine and Prelude Ventures. Our team members are active learners who enjoy working on tough problems that can only be solved with a combination of technical innovation, hard work, and joy. We are looking for diverse, world-class talent to help us change the way the world uses energy.
We are looking for an Energy Modeling and Optimization Engineer to prototype and test new algorithms and features for our internal software suite, Formware. This role will involve customer interaction to determine feature requirements as well as an ability to research academic work and adapt to specific real-world problems. You will be working closely with the business development team to determine future needs as well as software engineers to help incorporate prototype results into our core software package. A successful candidate will have experience with linear, mixed-integer and mathematical programming and optimization techniques in general. Foundational elements of data science are required, but the successful candidate will be encouraged to learn and apply additional techniques in the role. An introductory understanding of energy markets would be preferred.
What You'll Do
- Work with the BD and analytics team to guide Formware’s core feature set
- Contribute to the development of a differentiated and powerful suite of tools for asset optimization and planning
- Prototype new algorithms and develop them to be incorporated into our full software package
- Develop methods to perform optimal sizing and day-ahead and/or real-time scheduling in the presence of uncertainty
- Develop tools to simulate market features and operation over an array of possible futures scenarios
- Generate statistically and physically relevant future scenarios using both historical and forecasting data
- Collaborate with business development team to perform analyses and generate external reports/decks.
What We're Looking For
- MS/PhD in an Engineering, Computer Science, Operations Research or a related field
- 2+ years industry or academic experience with mathematical optimization applied to complex, multi-dimensional problems, with proven track record of bringing innovative contributions to the field
- Ability to define required data and understand how to best use externally provided data
- Experience surveying literature, rapid-prototyping algorithms and generalizing them for a broader set of problems
- Understanding of mathematical programming, building and fitting models using regression or other techniques.
- Willingness to learn new computational techniques and understand energy market features.
- Comfortable in Python, some Matlab experience is a plus.
- Enjoy the fast paced, results driven environment of a startup
- Data-driven decision maker
- Drives projects to completion
- High attention to detail
- Comfortable independently advancing experiments from conception through data analysis
- Excellent organizational, communication, and presentation skills a must!Hands-on approach to engineering problem solving
- Flexible working style: Can “wear many hats” and jump quickly between projects
- Able to work well in cross-disciplinary project teams and deliver results
At Form Energy, we are working toward a 100% renewable energy future for everyone in the world. We are committed to creating an inclusive environment for all our employees and are seeking to build a team that reflects the diversity of the people we hope to serve with our revolutionary products. Form Energy is proud to be an equal opportunity employer.