Data Scientist

Tyba

1d•$130,000 - $170,000•Hybrid

About The Position

Tyba is a modeling platform for energy companies developing, financing, and operating renewable energy infrastructure. Energy companies rely on technical models daily to make crucial infrastructure decisions. Our mission is to make cutting-edge models accessible to cross-functional teams such that companies can build and operate more renewable energy more profitably. We apply data science, AI/ML prediction, optimization, and physical modeling to a range of applications that support decision-making, from early-stage siting of power plants to the daily operations of energy storage and solar projects. Our customers access these models via an easy-to-use web application or programmatically through our API and rely on the accuracy of our models and the performance of our software. We are looking for a data scientist to join our team to work on modeling initiatives that deliver value to customers of our battery auto-bidding platform. You will excel in this role if you're passionate about clean energy, are a quick learner, have a strong sense of ownership, and are excited to learn about wholesale power market operations. As a member of the Modeling and Optimization team at Tyba, you will have the opportunity to contribute to a mission-critical product that synthesizes price forecasts and bid optimization algorithms to deliver strong returns for our customers. You will work on a cross-functional team, going deep on the intricacies of power markets to help improve our predictive models. This role primarily involves working on Tyba’s price forecast engine, with a focus on hypothesis-driven model experimentation.

Requirements

Master’s degree in CS/Statistics/Finance/Operations Research OR 2 years of experience working in related fields
Passion for working in clean energy and a strong willingness to build knowledge of power market fundamentals
Experience with Python and its package ecosystem (Pandas, PyTorch, plotting libraries), as well as SQL
Experience with time series forecasting, ideally at high frequency and demonstrated by concrete projects
Comfortable with machine learning models and concepts
Comfortable working in Git
Ability to work cross-functionally on an interdisciplinary team

Nice To Haves

Experience with energy and/or financial data, optimization and data infrastructure is a plus
Experience with working on ML systems in production is a plus

Responsibilities

Train and evaluate forecasting models for new applications and use cases.
Develop features for nodal electricity price forecasts, working with power market experts to identify predictive signals and integrate them into our forecasting infrastructure.
Investigate model behavior in specific contexts, such as diagnosing forecast misses and identifying their root causes.
Benchmark model performance by analyzing price forecast and battery dispatch backtests, defining the metrics that matter, and building dashboards to track them over time.
Communicate model performance and behavior clearly to internal stakeholders and customers.