Application Guide

How to Apply for Principal Data Scientist

at Electra

🏢 About Electra

Electra is uniquely positioned as Europe's leading high-speed EV charging network operator, directly accelerating the transition to sustainable transportation. Their focus on building a vast, reliable charging infrastructure addresses the critical barrier to EV adoption, making this role impactful at a systemic level. Working here means contributing to tangible climate solutions while tackling complex engineering challenges at scale.

About This Role

This Principal Data Scientist will own the data science strategy for Electra's physical charging products, connecting lab testing data to real-world reliability outcomes. You'll build models predicting charger degradation, failure modes, and lifetime performance to enable faster product iteration and informed engineering tradeoffs. Your work directly impacts product reliability, customer experience, and Electra's competitive advantage in the EV charging market.

💡 A Day in the Life

You might start by analyzing overnight charging station performance data to identify potential degradation patterns, then meet with Reliability engineers to discuss field failure reports and correlate them with lab test results. In the afternoon, you could refine a survival analysis model predicting charger lifetime based on usage patterns and environmental factors, followed by presenting findings to Product leadership to inform next-generation charger design decisions.

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Interview Prep

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🎯 Who Electra Is Looking For

  • Has 15+ years specifically applying data science to complex engineered systems (not just software), with deep experience in reliability statistics, survival analysis, and degradation modeling
  • Can demonstrate experience connecting lab/experimental data to field performance outcomes, with examples of metrics they established for product health monitoring
  • Possesses strong signal processing and time-series analysis skills applicable to charger performance data, not just generic machine learning expertise
  • Has built production models for physical systems (not just digital products) using Python/SQL and can discuss deployment challenges with engineering teams

📝 Tips for Applying to Electra

1

Highlight specific experience with reliability modeling for physical hardware systems (EV chargers, automotive components, industrial equipment, etc.) - not just software reliability

2

Prepare concrete examples of how you've connected lab test data to field performance outcomes, including metrics you established and how they influenced product decisions

3

Tailor your resume to emphasize signal processing and time-series analysis experience relevant to charger performance monitoring and anomaly detection

4

Research Electra's current charging network and mention specific aspects of their technology or expansion plans that interest you

5

Demonstrate understanding of the EV charging industry's reliability challenges (downtime costs, maintenance logistics, customer trust implications)

✉️ What to Emphasize in Your Cover Letter

['Your experience with reliability statistics and degradation modeling specifically for engineered physical systems', 'Examples of establishing product health metrics and leading indicators that were adopted across engineering teams', "How you've partnered with Product and Reliability teams to make data-driven tradeoffs in previous roles", "Why you're passionate about applying data science to accelerate EV adoption and sustainable transportation"]

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🔍 Research Before Applying

To stand out, make sure you've researched:

  • → Electra's current charging network footprint across Europe and their expansion plans
  • → Technical specifications of their charging products and any public information about reliability or performance
  • → The competitive landscape of European EV charging networks and reliability challenges in the industry
  • → Electra's leadership team and their backgrounds in automotive/energy/engineering sectors

💬 Prepare for These Interview Topics

Based on this role, you may be asked about:

1 Walk me through how you would model charger degradation using survival analysis given lab test data and field performance data
2 How would you establish leading indicators for charger failure modes, and what data would you need from Test and Reliability teams?
3 Describe a time you built a model connecting experimental data to real-world outcomes - what challenges did you face and how did you validate results?
4 How would you approach anomaly detection for thousands of distributed charging stations with varying usage patterns?
5 What metrics would you propose to measure product health for EV chargers, and how would you drive adoption of these metrics across engineering teams?
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⚠️ Common Mistakes to Avoid

  • Focusing only on software/data product experience without demonstrating understanding of physical system reliability
  • Presenting generic machine learning projects without specific examples of reliability statistics or degradation modeling
  • Failing to show how you've collaborated with hardware engineering or reliability teams in past roles

📅 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 Electra!

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