Optimization Engineer

Summary

Fermata Energy is seeking an Optimization Engineer to develop and deploy economic dispatch models for their vehicle-to-grid systems. The role involves collaboration with various teams, conducting financial analyses, running experiments, and contributing to the company's codebase. The candidate should have a technical degree, proficiency in Python, strong communication skills, and expertise in energy systems modeling and economic analysis.

Requirements

• Bachelors or advanced degree in operations research, computer science or a related technical field
• Proven experience in formulating and solving optimization models, including Linear Programs (LPs) and Mixed-Integer Linear Programs (MILPs)
• Strong proficiency in Python and object-oriented programming
• Excellent written and verbal communication, including producing effective and compelling data visualizations
• Subject matter expertise in energy systems modeling and economic analysis
• A self-starter who is comfortable taking ownership in a fast-paced startup environment

Responsibilities

• Work closely with the optimization and machine learning teams to collaboratively develop and improve optimization models
• Partner with internal business units to conduct in-depth financial analyses and simulations, aiding in data-driven decision-making and growing Fermata’s customer base
• Construct and run experiments to understand and improve our system’s performance for various grid services
• Run experiments to tune algorithms to maximize performance of deployed assets
• Share and convey your analyses, technical concepts, and recommendations to stakeholders, ensuring that your insights are clearly understood and impactful in guiding the organization

Preferred Qualifications

• Familiarity with distributed energy resources, electric vehicles (EVs), or energy markets and APIs such as Genability
• Experience with advanced optimization topics such as stochastic optimization, optimal control, decomposition methods, and meta-heuristics
• Experience with open source and commercial solvers and interfaces such as Gurobi, CPLEX, or cvxpy
• Proficiency in machine learning, including forecasting and uncertainty quantification
• Prior experience in a client-facing role as a consultant or in a similar capacity is beneficial
• Familiarity with database technologies, including PostgreSQL, Cassandra, and data engineering best practices
• Experience with a cloud platform, such as AWS, Azure, or GCP, is a plus
• Knowledge or experience with git, Scala, Docker, Kubernetes, Argo, as well as familiarity with Agile methodologies and tools like Jira, would be advantageous