Staff Data Science Engineer

REsurety•Boston, MA

6d•$156,400 - $170,000•Onsite

About The Position

Powering the grid with clean energy is one of the most complex challenges of our generation. Resurety is solving it by combining deep power market expertise with world-class engineering. We’re looking for a Staff Data Science Engineer to own the technical evolution of our core modeling systems. This isn’t a role for theoretical exercises; it’s a role for building robust, scalable software that accelerates the energy transition in real-time. Decarbonizing the grid is a data problem. At Resurety, we are building the financial and technical infrastructure required to transition the world to carbon-free energy. We don’t just track the market, we provide the certainty needed to fund the future of the planet. We are looking for a Staff Data Science Engineer to be the architect of that certainty. You will move our sophisticated research out of the lab and into the production-grade modeling infrastructure that powers our core business. As a high-leverage technical leader, you will bridge the gap between complex power market research and scalable engineering reality. This is a senior individual contributor role focused on technical strategy, system design, and cross-functional influence, not people management. You will be the primary technical partner to our Research and Power Markets teams, ensuring that our most ambitious analytical ideas are built on a foundation of rigorous, performant, and maintainable software. As a Staff Engineer at Resurety, you will be a technical leader responsible for the architecture, maintenance, and rigorous validation of the models and tools that power

Requirements

Bachelor’s or Master’s degree in a technical field; PhD-level research is valued but must be paired with significant industry experience.
8+ years of professional experience building, deploying, and maintaining data-intensive software systems in commercial production environments.
Expert-level proficiency in Python (Pandas/NumPy) and SQL, with an emphasis on writing maintainable, production-ready code
Significant experience with time-series modeling, forecasting, machine learning, and statistical techniques in applied settings (e.g., Statsmodels, Scikit-learn, TensorFlow, or PyTorch).
Hands-on experience with cloud infrastructure (AWS, GCP, or Azure), including containerized workloads (Docker/ECS) and Infrastructure-as-Code (e.g., Terraform).
Proven ability to lead technical initiatives across teams, making architectural decisions that balance correctness, performance, and maintainability.

Nice To Haves

Domain expertise in clean energy, power markets, or energy analytics.
Experience in finance, offtake structures, or hedging is a strong plus.
Experience building or supporting power flow or least-cost optimization models.
Prior experience operating at a staff or principal level, leading cross-functional technical strategy and initiatives.

Responsibilities

Lead the end-to-end design of our modeling infrastructure from Snowflake/Postgres schema design to production deployment ensuring our systems are modular, scalable, and resilient.
Serve as the primary bridge between exploratory research and production-grade software. You will transform sophisticated time-series forecasts (energy prices, emissions, load) into robust, automated modeling workflows.
Establish the "Gold Standard" for technical excellence. You will define and advocate for best practices in automated testing, data validation frameworks, and CI/CD patterns across the organization.
Conduct deep-dive analyses into complex meteorological and power market datasets to surface architectural improvements and modeling breakthroughs that directly impact our customers.
Act as a high-leverage technical authority and mentor. You will guide senior engineers through complex system-level tradeoffs and influence our long-term technical roadmap without the overhead of people management.