How to apply for Senior Data Scientist, Grid and Market Modeling

About Sunairio

Sunairio is a climate-tech startup that turns high-resolution weather and climate data into probabilistic energy market forecasts, helping traders and grid operators manage volatility and extreme events. Their flagship product, Sunairio ONE, generates thousands of realistic scenarios (ensembles) to quantify uncertainty, making them a key player at the intersection of climate risk and energy markets. Working here means tackling some of the most complex, high-stakes problems in energy with a small, impact-driven team.

About the role

As a Senior Data Scientist for Grid and Market Modeling, you will design, build, and deploy models that simulate grid operations and electricity market dynamics under deep uncertainty. You'll translate weather-driven supply and demand scenarios into actionable economic insights for traders and developers, directly shaping Sunairio's core product. This role is highly autonomous and requires a builder's mindset—you'll identify bottlenecks in existing models and fix them without waiting for a roadmap.

A typical day

You might start by reviewing the latest ensemble forecasts and identifying anomalies that could affect market prices. Then you'll dive into code to improve a scenario generation module, collaborating with engineers to optimize performance. Later, you'll join a product meeting to discuss how a new feature could help traders hedge against extreme events, and you'll prototype a solution by end of day.

Who Sunairio is looking for

• **Quantitative builder with energy market expertise:** You have hands-on experience modeling power systems, electricity markets, or grid operations, likely in a production environment (e.g., at an ISO/RTO, utility, hedge fund, or energy analytics firm). You're comfortable with economic dispatch, unit commitment, or locational marginal pricing concepts.
• **Probabilistic modeling and ML proficiency:** You can implement and improve ensemble-based forecasting, scenario generation, and uncertainty quantification using Python (or similar), and you know how to balance model complexity with computational efficiency.
• **Commercial intuition:** You prioritize models that drive trading decisions, risk management, or asset optimization—not just academic metrics. You understand how your work translates to P&L or operational value.
• **Founder mindset:** You thrive with high autonomy, proactively identify bottlenecks, and ship solutions quickly. You're a technical polymath who can jump between data engineering, model development, and stakeholder communication.

Tips for this application

• Highlight any experience with electricity market simulation or grid modeling (e.g., production cost models, security-constrained economic dispatch) and explicitly connect it to probabilistic forecasting or scenario analysis.
• Showcase projects where you deployed models into production and measured their commercial impact—quantify results in terms of trading performance, cost savings, or risk reduction.
• Demonstrate familiarity with ensemble methods and extreme event modeling, especially in the context of weather or climate data. Mention specific tools (e.g., XGBoost, PyTorch, Dask) and how you've used them at scale.
• Emphasize your ability to work autonomously: describe a time you identified a critical model bottleneck and fixed it without being asked, and how that improved outcomes.
• Tailor your resume to include keywords from the job description like 'probabilistic modeling,' 'grid and market outcomes,' and 'commercial intuition' to pass ATS and show alignment.

What to cover in your cover letter

["Your specific experience with grid or market modeling and how it aligns with Sunairio's mission to translate weather risk into economic outcomes.", 'A concrete example of a model you built or improved that had a direct commercial impact (e.g., improved trading strategy, reduced forecast error for extreme events).', 'Your approach to working autonomously and proactively—how you identify bottlenecks and drive projects forward without a detailed roadmap.', "Why you're excited about Sunairio's ensemble-based approach and the challenge of quantifying uncertainty in energy markets."]

Research before applying

• Explore Sunairio's website and any public materials (blog posts, whitepapers) to understand their ensemble generation methodology and how they differentiate from traditional weather forecasting.
• Research recent extreme weather events and their impact on energy markets (e.g., Uri, CAISO heat waves) to speak fluently about the problems Sunairio solves.
• Investigate the background of the founding team and their technical publications or talks to understand their vision and technical stack.
• Look into competitors like Amperon, Myst AI, or traditional weather firms (e.g., NOAA, DTN) to articulate what makes Sunairio unique.

Likely interview topics

Based on the job description, expect questions about:

• How would you model the impact of a severe winter storm on ERCOT electricity prices using probabilistic scenarios? Walk us through your approach.
• Describe a time you improved an existing model's performance. What was the bottleneck, and how did you address it? What was the commercial outcome?
• What are the key challenges in generating thousands of realistic scenarios for grid operations, and how would you ensure computational feasibility?
• How do you validate a probabilistic forecast model for energy markets? What metrics do you prioritize beyond RMSE?
• Given Sunairio's focus on extreme events, how would you incorporate climate change trends into your modeling framework?

Common mistakes to avoid

• Focusing too much on academic research or Kaggle competitions without demonstrating production deployment or commercial impact.
• Showing a lack of energy market knowledge—e.g., not understanding basic concepts like LMP, ancillary services, or unit commitment.
• Presenting yourself as needing clear direction or a detailed roadmap; Sunairio values high autonomy and self-starters.