Senior ML Engineer

This is a fully remote position, open to applicants in United States.

📋 Description

• Design, develop, train, and sustain production machine learning models.

• Identify, collect, and validate datasets relevant to the power sector, addressing uncertainties and data deficiencies.

• Create analytical strategies to address data shortages.

• Design, execute, and uphold comprehensive ML pipelines for energy market forecasting and grid analytics.

• Ingest data from Independent System Operators (ISO), utility providers, and weather data sources.

• Conduct feature engineering, model training, assessment, deployment, and monitoring.

• Productionize forecasting and optimization models for nodal price, load, and renewable generation.

• Establish robust testing, observability, documentation, and automated evaluation processes.

• Collaborate with software engineers, data scientists, energy economists, power systems engineers, and energy market analysts.

• Convert research findings and domain knowledge into scalable forecasting and optimization systems.

• Participate in technical design reviews, establish coding standards, and mentor best practices.

⛳️ Requirements

• Proven experience in building and deploying machine learning models within a production environment, particularly in power trading, energy procurement, commodity risk management, utility strategy, energy markets, or similar roles.

• A master’s degree or Ph.D. in applied statistics, applied economics, computer science, or a related discipline.

• Ability to monitor real-time data pertaining to wholesale power markets, gas markets, congestion, basis, capacity, ancillary services, tariff changes, and regulatory developments that may impact portfolio costs or reliability.

• Proficient in Python programming.

• Practical experience with machine learning frameworks and time-series forecasting techniques as applied to the power sector.

• Strong grasp of current machine learning methodologies, including large language models (LLMs), predictive analytics, and agent-based modeling.

• Familiarity with time-series challenges such as non-stationarity, seasonality, preventing data leakage, and employing suitable cross-validation methods.

• Capability to effectively communicate model outputs and technical trade-offs to non-technical audiences and clients.

• Experience thriving in a dynamic, ambiguous environment where product offerings may adapt based on client requirements.

🏝️ Benefits

• Extensive nationwide medical, dental, and vision insurance.

• Flexible vacation policy along with ten company-wide holidays.

• Annual winter break spanning from Christmas to New Year's.

• 16 weeks of fully paid parental and family leave without any tenure prerequisites.

• Remote-friendly work culture complemented by annual company-wide retreats.

• Reimbursement for work-from-home setup expenses.