Application Guide

How to Apply for Senior Data Scientist

at Omnidian

🏢 About Omnidian

Omnidian is a mission-driven company at the forefront of the clean energy transition, providing 24/7 monitoring and diagnostics for solar and storage systems. Their Resolv platform uses advanced data science to maximize renewable energy production, directly contributing to a sustainable future. Working here means applying your skills to solve real-world climate challenges.

About This Role

As Senior Data Scientist, you'll lead the development of machine learning models that detect underperformance and faults in PV and storage time-series data. Your work will directly impact the reliability and efficiency of solar energy systems, helping customers and the planet. You'll own the full data science lifecycle, from problem definition to deployment and monitoring.

💡 A Day in the Life

You'll start by reviewing model performance dashboards for deployed anomaly detectors, then dive into a new dataset from a solar farm to develop diagnostic logic for a specific fault type. After a cross-team sync with engineering to plan integration, you'll prototype a state-space model in a Jupyter notebook, and end the day by documenting your approach for the team.

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Interview Prep

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🎯 Who Omnidian Is Looking For

  • Deep expertise in time-series analysis: anomaly detection, forecasting, seasonal decomposition, and state-space models, applied to sensor or energy data.
  • 6+ years of hands-on applied data science experience, with a track record of deploying production ML models and monitoring their performance.
  • Strong Python skills (pandas, numpy, scikit-learn) and SQL; experience with PyTorch or TensorFlow for deep learning models.
  • System thinker who can distinguish root causes of underperformance (e.g., soiling vs. shading) and build diagnostic logic.

📝 Tips for Applying to Omnidian

1

Highlight specific projects where you deployed time-series models in production, including how you handled model monitoring and retraining.

2

Showcase any experience with renewable energy data (solar irradiance, inverter data, battery storage) – even from personal projects or open-source contributions.

3

Emphasize your ability to partner with engineering teams: include examples of integrating models into a platform or API.

4

Tailor your resume to mention anomaly detection and fault diagnosis explicitly, as these are core to the Resolv platform.

5

Prepare a short case study or portfolio piece demonstrating your approach to a time-series problem from data exploration to deployment.

✉️ What to Emphasize in Your Cover Letter

['Your passion for using data science to combat climate change and enable renewable energy.', 'Specific experience with time-series anomaly detection and root cause analysis in complex systems.', 'Your track record of owning the full ML lifecycle and collaborating with engineering for production deployment.', 'How your skills can directly improve the reliability and performance of solar and storage systems at scale.']

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🔍 Research Before Applying

To stand out, make sure you've researched:

  • → Read about Omnidian's Resolv platform and its features for real-time monitoring and diagnostics.
  • → Look into common PV system faults (e.g., soiling, shading, inverter failures) and how they manifest in time-series data.
  • → Understand the company's mission and recent news about their impact on renewable energy adoption.
  • → Check if Omnidian has published any technical blogs or white papers about their data science approaches.

💬 Prepare for These Interview Topics

Based on this role, you may be asked about:

1 Walk me through your approach to detecting anomalies in high-frequency time-series data from thousands of sensors.
2 How would you distinguish between soiling, shading, and inverter clipping as root causes of underperformance?
3 Describe a time you deployed a model into production and how you monitored its performance over time.
4 How do you handle concept drift or data quality issues in a streaming time-series context?
5 Explain how you would design a diagnostic logic pipeline to scale across diverse solar installations.
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⚠️ Common Mistakes to Avoid

  • Don't focus solely on deep learning or computer vision – this role is heavily about time-series and diagnostics.
  • Avoid vague claims about 'machine learning' without concrete examples of production deployment and monitoring.
  • Don't ignore the renewable energy context; failing to show genuine interest in sustainability can be a red flag.

📅 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 Omnidian!

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