Machine Learning Scientist

Summary

Join Equilibrium Energy, a Series B clean energy startup, as a Staff/Sr. Staff Machine Learning Scientist. You will play a key role in developing and deploying machine learning models for energy asset management and financial trading. Responsibilities include shaping product direction through research insights, formulating novel machine learning solutions, and evaluating model performance. This position requires an advanced degree in a quantitative field, significant experience in machine learning and the energy domain, and strong Python skills. We offer a competitive salary, comprehensive benefits, unlimited vacation, flexible work schedule, remote work options, and opportunities for professional growth.

Requirements

• Passion for clean energy and fighting climate change
• An advanced degree in computer science, data science, machine learning, artificial intelligence, operations research, engineering, or related quantitative discipline
• 4+ years experience in data science, research science, machine learning, or similar role, applying and adapting deep learning, graph neural networks, or reinforcement learning techniques to time series regression & forecasting problems
• 2+ years experience in the electricity & energy domain (e.g. electricity price forecasting, congestion prediction etc)
• 3+ years experience with python and the supporting computational science tool suite (e.g. numpy, scipy, pandas, scikit-learn, tensorflow, etc.)
• Experience developing, releasing, and tracking performance of ML models in production
• Experience communicating mathematical concepts, analytical results, and data-driven insights to both technical and non-technical audiences
• A collaboration-first mentality, with a willingness to teach as well as learn from others

Responsibilities

• Use research insights to shape product direction
• Influence product and engineering roadmaps through presentation of research insights, experimental results, and model performance metrics, in order to evolve organizational direction
• Initiate and lead cross-functional engagements to surface, prioritize, formulate, and structure complex and ambiguous challenges where advanced novel deep learning research can have outsized company impact
• Formulate and apply novel machine learning solutions to the energy domain
• Tackle complex deep learning & machine learning problems by researching published academic literature, surveying industry techniques & intuition, and executing hands-on experimental testing & modeling
• Drive the design, specification, development, and production deployment of our suite of novel deep learning & machine learning solutions
• Lead short to medium term research projects that advance the state-of-the-art in deep learning as applied to energy asset management and financial trading
• Define and evaluate a suite of success metrics across our portfolio of candidate and deployed machine learning models in order to understand operational characteristics, diagnose sources of under-performance, and identify opportunities for further research & improvement

Preferred Qualifications

• Experience designing and building novel statistical models on time series data, including characterizing probabilistic outcome uncertainty
• Experience with dimensionality reduction, component decomposition, or embedding space analysis & visualization techniques (e.g. UMAP, T-SNE, Autoencoder)
• Experience with model explainability methods (e.g. SHAP)
• Experience with database technologies and sql
• Experience with probability, hypothesis testing, and uncertainty quantification
• Experience with optimization techniques (e.g. stochastic optimization, robust optimization)
• Experience with data visualization and dashboarding technologies (e.g. plot.ly Dash, Streamlit)
• Experience leading and mentoring a team of scientists
• Demonstrated track record of academic paper or social media publication

Benefits

• Competitive base salary and a comprehensive medical, dental, vision, and 401k package
• Opportunity to own a significant piece of the company via a meaningful equity grant
• Unlimited vacation and flexible work schedule
• Ability to work remotely from anywhere in the United States, Canada & Europe, or join one of our regional hubs in Boston, SF Bay Area, or London
• Accelerated professional growth and development opportunities through direct collaboration and mentorship from leading industry expert colleagues across energy and tech