Comity is looking for a Machine Learning Engineering Manager to lead a team developing our energy pricing models and working on their deployment in market-leading power contract origination strategies.

In this role, you'll be a hands on leader, working alongside your team and closely with our Quantitative Researchers to design and deploy models and optimization algorithms for participating in wholesale power markets, and ensure that they can scale to new markets and greater volume. You will help develop our data infrastructure and research platform, which give our researchers streamlined access to relevant data sources and powerful computing resources, as well as the confidence that their research will translate into real-world results. As an early hire in this role, you will have broad impact and ownership over our platform and framework design, research agenda, technology choices, and team culture.

We’re excited about you because:

-- You have 8-12 years of experience developing machine learning models (or other predictive models) in an industry and/or academic setting.

• You are aligned with an onboarding plan that includes some time as an individual contributor to learn our domain and context.

• You have experience leading and mentoring ML engineers/researchers, software engineers, and/or data scientists.

• You have a solid grasp of machine learning fundamentals and can read research results to understand operational requirements and assumptions.

• You have experience with probabilistic forecasting models for multivariate time series and other structured data. Experience applying ML in financial settings or with graph and/or spatial data is a plus.

• You are experienced and comfortable with the workflow to research and develop machine learning models and data mining algorithms, and working with distributed computing resources.

• You are a lifelong learner and empathetic teacher. We’re committed to the growth and development of our teammates. We work towards a shared understanding by listening with intent and holding open discussions because we know that's how we'll deliver quality results.

• We believe strong foundations are more important than specific technical knowledge, but experience with any part of our data stack (Python, SQL, PyTorch, CatBoost) is a plus.