San Francisco, CA /
Growing Energy Labs, Inc (Geli) – Technology - Data Science /
This is a San Francisco based position that is currently remote and will have a hybrid schedule once we return to office. We are open to candidates willing to relocate to the San Francisco Bay Area.
Geli (Growing Energy Labs, Inc.) provides software and business solutions to design, connect, and operate energy storage systems ranging in size from residential to utility-scale, as well as grid-tied, microgrid, and off-grid systems. Geli’s suite of products creates an ecosystem where project developers, OEMs, financiers, and project operators can deploy advanced energy projects using a seamless hardware-agnostic software platform.
Geli is a subsidiary of Hanwha Q CELLS, one of the world’s largest photovoltaic manufacturers most recognized for its high-performance, high-quality solar cells and modules.
Geli is committed to helping make the planet a cleaner, better place to live, both with our software products and through our everyday actions.
Imagine a world where there is less reliance on non-renewable power, where you source your electricity from your neighbors rather than from power stations hundreds of miles away and software makes the best possible use of the solar, wind, and battery storage available. This is our vision.
We are looking for enthusiastic colleagues that are not only fluent in technology, but also share our vision of a world running on 100% renewable energy.
ABOUT THE JOB
Geli is looking for an experienced optimization engineer with a software development background in Python to join our Data Science team. Geli is innovating the way distributed energy resources are controlled, and the optimization engineer will help refine and develop the algorithms which are at the heart of it all.
As applications of energy resources continue to proliferate and evolve, our formulation of economic objectives needs to be updated to reflect these changes. The optimization engineer will formulate these new objectives, refine methods to hedge against uncertainty, and collaborate with our forecasting data scientists to maximize expected savings. Our data analytics and cloud platforms use state-of-the-art technologies to set you up for success. Since optimization is at the core of Geli, you will have plenty of opportunities to learn about and contribute to the software development of our other energy storage software products.
We are looking for a motivated self-starter that thrives in a dynamic environment. A strong work ethic with excellent problem-solving skills are equally important as being a team player who enjoys to learn and teach.
You will work closely with the rest of the data science, product, and software engineering teams to execute job duties listed in "Primary Responsibilities". For this position, we are open to remote work.
- Collaborate with product managers to understand program requirements and market rules
- Translate program and market rules into DER (Distributed Energy Resources) scheduling and dispatch optimization models including wholesale electricity markets, demand response, behind the meter incentives and rules
- Develop constraints and penalties to drive heuristic behaviors Develop, unit test, and document object-oriented Python code Write Python code to manipulate and analyze timeseries data
- Collaboratively review and revise pull requests with your teammates
- Collaborate with the forecasting team on probabilistic forecasting and optimization approaches
- Contribute to the development and maintenance of our simulation codebase that we use for evaluating algorithm performance and DER economics
- Conduct batch simulation experiments to tune models and parameters for maximum performance
- Troubleshoot optimizer software operational issues and use operations data to improve algorithms
- Provide support to sales engineers and operations on the best optimizer software configuration
- M.S. in science or engineering, or equivalent combined education and work experience)
- 3+ years of industry experience developing optimization models for energy applications.
- Experience with optimization modeling packages such as PYOMO, CVXPY, or solver APIs (e.g. Gurobi, Cplex, Xpress)
- Experience with Python libraries for numerical methods and timeseries data (NumPy, Pandas, SciPy, datetime)
- Strong theoretical background on Linear Programming, MIP, and Convex Optimization and solution algorithms
- Strong background in linear algebra and statistics
- Experience in collaborative software development environment (version tracking with Git, code reviews) Ability to write clean, maintainable, tested and shippable production code
- Ability to cooperate with other engineers, and focus on team goals
- Knowledge of machine learning algorithms (time series forecasting, clustering algorithms, probabilistic models, supervised and unsupervised learning)
- Databases: PostgreSQL, Django
- Knowledge of Cloud, IoT technologies, and scalable platforms (Docker, AWS, Kubernetes) As a member of the team you will have significant influence on the direction of our technology stack.
BENEFITS OF WORKING AT GELI
Competitive salary commensurate with experience
Competitive benefits offerings
Convenient accessible location in downtown San Francisco
Flexible work-from-home-office opportunities, as determined by position and job duties
Make a difference: join a group of people who are passionate about renewable energy
Have an impact: the company is still small enough that everyone’s contribution has a significant impact to the success of the company
Many opportunities to lead teams, projects, and contribute to development
Casual professional working environment: there’s no need to dress up, just present your best self
Work collaboratively in a diverse environment- we commit to reaching better decisions by respecting opinions and working through disagreements
We value the insights that a diverse team can bring. We encourage applications from members of groups that have been traditionally underrepresented in tech.
Growing Energy Labs, Inc. provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability, or genetics.