In the next 30 years, the world will transform every part of the built environment to be climate positive green infrastructure. Knowing what, where, and how to build infrastructure like solar farms is one of the great opportunities of our time. However, there are problems!
The Problem
80% of clean energy projects that developers start never actually get built because most projects are started without deep due diligence on zoning and interconnection due to the cost of collecting that data. This means $17B worth of canceled projects per year.
Our Solution
Paces is software for green infrastructure developers to identify the best places to build and manage their projects. First we collect environmental, permitting, zoning, energy grid data from various different sources; then we analyze the data and use AI to identify the best places for developers to build their next projects.
Our Team
We are building a team where people can proudly say their time at Paces is the
most impactful, meaningful work of their career. Our amazing team in Brooklyn, New York includes incredible engineers and growth team members from companies like Facebook, AWS, Replika, DataDog, Yotpo, Rent The Runway & Leap.
Paces is growing rapidly and looking for exceptional people to join who want to have a massive positive climate impact while building a great culture!
We are looking for a Data Scientist with deep expertise in energy system modeling to join our Product team, reporting directly to the Head of Product. This role is a unique opportunity to bridge power engineering with cutting-edge software and product development.
🏆 What You’ll Achieve
- Model transmission system dynamics with rapid load growth under high penetrations of renewables
- Scrape and process generation and transmission upgrade queues, utility planning documents, and other unstructured and semi-structured data sources
- Probabilistically model interconnection scenarios and develop technically rigorous study assumptions
- Analyze output data from power flow simulations to consider the technical and commercial viability of new generation and large loads and identify the most favorable locations
- Develop software solutions and data pipelines that enhance and automate power system analysis at scale
- Act as an internal domain expert at Paces, developing proprietary methodologies and shaping product development
📈
Requirements
- 3+ years experience in data science, research science, machine learning, or a similar technical role
- 3+ years experience with python and the supporting computational and geospatial science tool suite (e.g. numpy, scipy, geopandas, GDAL, scikit-learn, tensorflow, etc.)
- 4+ years experience in the electricity and energy domain (e.g. electricity grid modeling and optimization, power system planning and operation, energy markets and regulation, price forecasting, congestion prediction, etc.)
- Familiarity with at least one major ISO/RTO or wholesale market (e.g., PJM, MISO, ERCOT, CAISO, WECC, etc.).
- Familiarity with at least one third party power flow or production cost modeling software (PSS/E, TARA, PLEXOS, etc.)
- Strong quantitative and data wrangling abilities
- Ability to think beyond traditional engineering approaches and contribute to product innovation through creative, iterative thinking
✨ About You
You will thrive in our culture if you:
- You love deep in person collaboration
- You get a thrill out drawing insights from messy data
- You have a strong bias towards action and a high learning rate
- You are always looking for better ways to automate tedious tasks
🚀 Bonus Points
- Previous experience at a high-growth, fast-paced startup
- Previous experience at a battery storage, solar, or datacenter developer
- Degree in electrical or power engineering
- Expertise in energy policy and regulatory analysis
💰 Compensation And Benefits
- $130,000 - 150,000 annual compensation
- Competitive equity compensation
- 401(k) match (4%)
- Health, dental and vision insurance
- Paid company holidays & unlimited PTO (minimum 15 days per year)
- Hybrid work in the office in Williamsburg, Brooklyn 2-4x per week
