Principal Data Scientist

Who we are:  

Electra is reinventing ironmaking from the ground up with a breakthrough electrochemical technology that produces high purity clean iron and critical co-minerals. With two operating pilots in Boulder and a new demonstration facility underway in Jefferson County, we’re on track to reach commercial scale by the end of the decade. Backed by over $300 million and strategic partnerships with global leaders across mining, steel, and big tech, momentum is on our side.  Recognized by Fast Company as a Most Innovative Company and a Next Big Thing in Tech, and named one of TIME’s 100 Climate Leaders, we’re building a company shaping the future of industry. If you’re looking to make an impact and join a team that’s driven, focused, and scaling, 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 stakeholder management skills with the ability to align diverse teams, drive adoption, and influence without authority
• Strong communication skills (written and verbal) with the ability to explain sophisticated methods and results to non-experts 
• Comfort operating in a fast-moving environment with shifting requirements, balancing near-term deliverables with long-term strategy 
• Experience working with sensor-heavy systems, industrial test stands, or manufacturing data at scale 

Compensation: