Market Data Scientist

As a Market Data Scientist, your primary responsibilities will involve transforming complex data into actionable insights. You will contribute to cross-functional projects that enhance market design, test proposed rules, and guide stakeholder discussions. Your teammates come from a variety of backgrounds - economists, engineers, data scientists, writers, business professionals and more. Collectively, we assign projects and project roles based on each person s expertise, passions, and goals. Candidates from all backgrounds are encouraged to apply.

What we offer you:

• Hybrid work schedule with 3 days/week onsite
• Relocation Assistance
• Base salary plus performance bonus program, professional development and tuition reimbursement, enhanced 401k and financial planning, wellness programs with onsite gym, onsite caf with free coffee, flexible work hours, access to business networks & more, all in a stable and supportive work environment!

How you will make an impact:

• Develop and maintain statistical and machine learning models to assess market outcomes, simulate policy changes, and forecast trends.
• Design ETL processes to extract and analyze very large datasets from the ISO s operational systems using SQL, Python, SAS, or R, with efficient post-processing workflows to support high-volume reporting and strategic analysis.
• Produce reports and dashboards that clearly communicate trends, anomalies, and insights to both technical and business stakeholders.
• Collaborate in cross-functional project teams focused on shaping market rules, implementation strategy, and stakeholder engagement.

What you need to be successful in this role:

• Bachelor s or master s degree in data science, Statistics, Computer Science, Operations Research, Economics, Math, Finance, or a related technical field.
• Proficiency in Python and either R or SAS.
• Proficiency with SQL.
• Strong understanding of core statistical concepts such as regression, probability, and hypothesis testing.

Desired not required:

• Experience working in energy, electricity markets, or large-scale policy- driven data environments.
• Knowledge of GAMS or other optimization modeling tools.
• Familiarity with version control (e.g., Git) and collaborative coding workflows.

• Prior exposure to data visualization or dashboard tools (e.g., Plotly, Dash, R Shiny).
• Experience with numerical simulations methods and implementation, neural networks, deep learning frameworks, or experimentation methods.