Principal Data Scientist

Who we are:  

We're transforming one of the world’s oldest industries with cutting-edge technology and an innovative approach. Backed by top-tier investors and recognized by Time as one of the "best Inventions of 2024" and Fast Company as one of 2024's "Next Big Things in Tech", Electra is scaling rapidly and we're looking for bold, driven individuals to help us reshape the future of iron production. If you're ready to make a real impact in a company that's redefining heavy industry for a cleaner, smarter world, we want to hear from you.

What you will do:  

Electra is seeking a Principal Data Scientist to join our Product team and elevate how we use data to guide product decisions, accelerate learning, and improve system performance and reliability. This role will partner closely with Test Engineering and Reliability Engineering while engaging lab-wide stakeholders across Process, Manufacturing, Quality, R&D, and Operations to build data products, models, and analytical frameworks that scale. 

Responsibilities include:

• Own and evolve Electra’s product data science strategy in partnership with Product leadership, Test, and Reliability teams
• Develop analytical frameworks that connect lab results → product performance → reliability outcomes, enabling informed tradeoffs and faster iteration 
• Establish metrics and leading indicators for product health (e.g., performance, degradation, failure modes, yield, stability) and drive adoption across teams
• Build and deploy advanced models for reliability and product behavior, including (as applicable): Survival analysis / Weibull, degradation modeling, and lifetime prediction 
• Anomaly detection and early warning systems for test stand and product performance drift 
• Causal inference / quasi-experimental methods to understand drivers of failure and performance changes 
• Translate model outputs into clear, decision-ready recommendations for technical and business stakeholders. 
• Partner with Test Engineering to improve test plans, sampling strategy, and experiment design (DOE) to maximize learning per test hour and reduce cycle time. 
• Define and promote standards for data quality, instrumentation signals, metadata capture, and repeatable analysis so results are interpretable and comparable across runs. 
• Collaborate with Reliability Engineering to strengthen failure mode analytics, root cause investigations, and reliability growth tracking. 
• Create scalable analytics tools and “data products” (dashboards, pipelines, notebooks, model services) used by stakeholders across the lab. 

What we need you to bring to the team:   

• Bachelor’s degree in computer science, statistics, engineering, or related fields 
• 15+ years of experience in applied data science for complex engineered systems, with focus on reliability modeling, test and experimental data 
• Proven track record delivering high‑impact data science solutions in product development, reliability, test engineering, or other complex physical systems (e.g., energy, manufacturing, industrial, hardware, chemicals/materials)
• Strong expertise in statistical modeling and machine learning, with depth in areas such as reliability statistics (Weibull, survival analysis), degradation and lifetime modeling, time‑series analysis, anomaly detection, and signal processing
• Advanced software and data skills, including Python and SQL, with experience building reproducible analytics workflows (version control, testing, documentation) and clear data visualizations and dashboards for technical and business audiences
• Familiarity with deploying analytics at scale (e.g., batch scoring, APIs, MLOps patterns) and translating analytical outputs into reusable tools and data products
• Demonstrated ability to drive high‑confidence decisions from imperfect or limited data, using sound assumptions, sensitivity analysis, and engineering judgment degradation analysis, and translating analytical insights into product and business decisions
• Exceptional sta