Application Guide

How to Apply for Senior Data Scientist

at Recurve

🏢 About Recurve

Recurve is a mission-driven company focused on accelerating clean energy through demand flexibility, aiming for a carbon-free grid. They leverage data science to optimize distributed energy resources, making them a unique player at the intersection of climate tech and advanced analytics. Working here means contributing directly to the energy transition with cutting-edge ML models.

About This Role

As Senior Data Scientist, you'll lead the development of statistical and ML models for load forecasting, flexibility, and customer behavior using AMI interval data. Your work will directly impact grid planning and program design, translating complex data into actionable insights for cross-functional teams. This role is pivotal in validating models with rigorous backtesting and communicating tradeoffs to partners.

💡 A Day in the Life

A typical day might involve analyzing AMI interval data to identify customer flexibility patterns, then iterating on a Python model with scikit-learn. You'll present findings to cross-functional teams, discuss tradeoffs with product managers, and validate model performance through backtesting. Afternoons could be spent coding SQL queries or collaborating with engineers to deploy models into production.

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Interview Prep

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🎯 Who Recurve Is Looking For

  • 5-8 years of post-Bachelor's experience in data science, with a focus on energy systems or related fields (advanced degree preferred).
  • Deep expertise in Python data science stack (pandas, NumPy, scikit-learn) and SQL, with hands-on experience in supervised and unsupervised ML.
  • Proven track record in applying ML to energy datasets, such as AMI interval data, demand response, or DERs.
  • Strong ability to lead complex analytic projects, validate models with backtesting and uncertainty analysis, and communicate findings to non-technical stakeholders.

📝 Tips for Applying to Recurve

1

Tailor your resume to highlight experience with AMI interval data, load forecasting, or energy flexibility models—generic ML projects are less relevant.

2

In your cover letter, explicitly mention your familiarity with demand response or distributed energy resources and how your work can help Recurve's mission.

3

Showcase a project where you validated a model with backtesting and uncertainty analysis, as this is a key requirement.

4

Use keywords from the job description like 'demand flexibility', 'carbon-free grid', and 'structured backtesting' to pass ATS filters.

5

Prepare a brief portfolio or GitHub link demonstrating Python and SQL work on energy-related datasets.

✉️ What to Emphasize in Your Cover Letter

['Emphasize your passion for clean energy and how your data science skills can directly contribute to a carbon-free grid.', "Highlight specific experience with AMI data or similar interval data and how you've used it for load or flexibility modeling.", 'Discuss your approach to model validation and communicating tradeoffs to cross-functional partners.', "Mention any experience with demand response or DERs to align with Recurve's core focus."]

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🔍 Research Before Applying

To stand out, make sure you've researched:

  • → Read Recurve's blog or case studies on demand flexibility and how they measure grid impact.
  • → Understand their platform and how they integrate with utilities for demand response programs.
  • → Research recent news on clean energy policy and how demand flexibility fits into carbon-free grid goals.
  • → Look into their team structure and any published research or talks by their data scientists.

💬 Prepare for These Interview Topics

Based on this role, you may be asked about:

1 Walk through a time you built a model for load forecasting using AMI data—what challenges did you face?
2 How do you validate models with backtesting and uncertainty analysis? Provide an example.
3 Explain how you would approach modeling customer behavior for demand flexibility programs.
4 Describe your experience with Python data science tools and SQL in an energy context.
5 How do you communicate complex model tradeoffs to non-technical stakeholders?
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⚠️ Common Mistakes to Avoid

  • Submitting a generic application without mentioning energy or demand flexibility—Recurve is niche.
  • Overemphasizing deep learning or NLP when the role focuses on statistical ML and energy data.
  • Neglecting to show model validation rigor; backtesting and uncertainty analysis are critical.

📅 Application Timeline

This position is open until filled. However, we recommend applying as soon as possible as roles at mission-driven organizations tend to fill quickly.

Typical hiring timeline:

1

Application Review

1-2 weeks

2

Initial Screening

Phone call or written assessment

3

Interviews

1-2 rounds, usually virtual

✓

Offer

Congratulations!

Ready to Apply?

Good luck with your application to Recurve!

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