Standard Potential Senior Data Scientist New York, NY · Full time Company website

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Standard Potential is an innovative energy storage startup deploying solutions to make power more affordable and reliable. Our mission is to expand access to load flexibility through distributed storage. We currently operate New York City’s largest indoor residential battery virtual power plant with Con Edison, as featured in Canary Media and CBS New York. Standard Potential is headquartered in the NYC energy and climate technology ecosystem and supported by prominent partners such as NYSERDA, Activate, and Breakthrough Energy.

Who We Are

Standard Potential is an early-stage, five-person grid technology startup deploying energy storage solutions to make electricity more affordable and reliable. Plug-in HVAC comprises 20% of Con Edison’s entire capacity: 2.5 GW of load. Our mission is to put a battery in every home, enabling those loads to be flexible through distributed storage. While the value of grid flexibility has historically been accessible only to wealthy homeowners (e.g. managed EV charging) and large commercial entities, we aggregate residential tenant loads to provide critical demand response services to the grid.

Standard Potential currently operates NYC’s largest indoor residential battery virtual power plant with Con Edison, as featured in Canary Media and CBS New York. Standard Potential is headquartered in NYC and supported by prominent partners such as NYSERDA, Activate, and Breakthrough Energy.

What to Expect

This role will drive the algorithmic core that operates our distributed battery fleet. You will be responsible for transforming high-frequency telemetry and customer usage data into predictive models and control logic that optimize battery dispatch against complex utility constraints. This requires a rigorous approach to data analysis, algorithm design, and economic optimization.

Beyond the technical execution, this is a fundamental leadership position. You will not just be executing tasks; you will be defining the architecture of our data systems and the logic of our fleet operations. As a key early hire, you will have the autonomy to define your own projects, establish sustainable engineering workflows, and eventually help build and lead the data team.

However, this opportunity carries the reality of an early-stage startup. Unlike a large tech company with established infrastructure and specialized support teams, you will often need to build your own tooling and navigate ambiguity. You must be comfortable wearing many hats–operating as both architect and implementer–to turn our technical vision into a scalable reality.

What You’ll Do

• Own the Model Lifecycle: Design and implement the end-to-end machine learning pipeline, from data ingestion and training infrastructure to deployment and monitoring.
• Design Control Algorithms: Engineer the logic that governs battery charging and discharging while optimizing for demand response revenues.
• Operationalize Utility Constraints: Analyze complex utility demand response manuals to rigorously translate baseline calculations and performance rules into programmatic constraints.
• Advanced Data Analysis: Query and manipulate large SQL datasets of time-series battery telemetry and utility meter data; identify data quality anomalies and drive schema improvements in collaboration with Data Engineering.
• Build the Experimentation Engine: Design and maintain the framework that enables operational A/B testing on physical assets, allowing us to rapidly iterate on dispatch strategies in the field.

What You’ll Bring

• Advanced Data Science Experience: 5+ years of professional experience in data science, with a focus on predictive modeling, timeseries forecasting, or algorithms.
• Python Proficiency: Expertise in Python (pandas, NumPy, scikit-learn) with experience deploying production-grade code.
• Database Expertise: Strong proficiency in SQL for extracting and aggregating high-volume time-series data; ability to formulate complex queries to isolate specific signal patterns.
• End-to-End Ownership: Demonstrated leadership in taking data products from zero to one–building the architecture, setting up the database, and deploying the model.
• Cloud & Infrastructure Fluency: Experience with cloud computing environments (e.g., AWS, GCP) and managing data pipelines/infrastructure.
• Rigorous Engineering Standards: A track record of establishing efficient, sustainable workflows for code quality, testing, and data governance.
• Mathematical & Analytical Rigor: Deep understanding of model selection and validation, with the ability to distinguish e.g. when to apply complex neural networks versus heuristic or regression-based approaches.
• Domain Alignment: A strong interest in energy systems, grid modernization, and decarbonization.
• Preferred Qualifications: Experience with optimization frameworks (Linear Programming/MILP), energy market dynamics, or physics-based modeling.

Compensation & Benefits

Competitive compensation package, equity, and comprehensive benefits, such as medical, dental, vision coverage, 401(k) plan, and flexible leave policy.

Equal Employment Opportunity

Standard Potential is proud to be an equal opportunity employer and is committed to creating an inclusive environment for all our employees and are seeking to build a diverse team that reflects the people and communities we hope to serve with our revolutionary technology.