Staff Engineer

Overview

Build the quantitative foundation for reliability decisions across the early and late stages of new product development, connecting mission profiles, reliability models, test, manufacturing, warranty and field evidence to fleet performance and cost.

What you'll do

• Translate customer use conditions, service-life expectations, availability needs, and business objectives into measurable reliability requirements, metrics, assumptions, and evidence plans.
• Develop and maintain product mission profiles covering geography, ambient and internal conditions, operating states, duty cycles, load levels, and exposure over time; document data sources and uncertainty.
• Build and maintain system and subsystem reliability models, including Reliability Block Diagrams and fault trees, to evaluate architecture, redundancy, dominant contributors, reliability allocations, and sensitivity to assumptions.
• Develop component and system reliability predictions using applicable parts-count, parts-stress, supplier, test, and field evidence, and clearly state limitations.
• Apply reliability statistics to life and event data, including Weibull and other life distributions, censored data, confidence bounds, accelerated-life relationships, reliability growth, and Bayesian updating where appropriate.
• Integrate analytical outputs with DFMEA, architecture reviews, design trade studies, and development gates so that quantified risk informs design priorities and readiness decisions.
• Develop and maintain a field reliability data model connecting product configuration and serial or cohort information with exposure, telemetry, service events, returns, failure analysis, and warranty claims.
• Define consistent metric definitions and denominators for field performance, including failure rate, cumulative failures, availability or downtime measures where applicable, and warranty incidence and cost.
• Analyze warranty, service, return, and field data to identify trends, shifts, recurring failure modes, affected populations, and emerging risks; quantify uncertainty and distinguish confirmed findings from hypothesis.
• Develop warranty life and cost analyses estimating claim frequency and cost over time, assess sensitivity to assumptions, and support product, finance and business planning.
• Build and maintain dashboards and recurring reports presenting reliability performance by product, configuration, region, age, exposure, and failure mode, with clear definitions and decision-focused interpretation.
• Track early-launch performance and reliability growth using pilot and field evidence; update predictions as exposure accumulates and communicate material changes to forecasts and risk outlooks.
• Feed field findings into future mission profiles, reliability requirements, model assumptions, design priorities, supplier actions, and validation evidence plans.
• Partner with Data Engineering and business system owners to improve data availability, traceability, quality, and repeatability across engineering, manufacturing, service and warranty sources.
• Use statistical and analytical tools such as Python, JMP or R and visualization platforms such as Power BI or Tableau to develop reproducible analyses and practical reporting workflows.
• Present results to technical and business audiences, explaining methods, assumptions, confidence, data gaps and recommended actions in clear written reports and product reviews.
• Coach engineering and business partners in interpreting reliability metrics and statistical evidence, including the implications of exposure, sample size, censoring and model uncertainty.

What you'll need

• Bachelor's degree in Engineering, Reliability Engineering, Applied Statistics, Data Science or a related quantitative field; equivalent experience may be considered.
• Typically 10+ years of experience in reliability engineering, reliability analytics, product quality analytics, or a related quantitative engineering role.
• Demonstrated experience with reliability modeling and statistics, including mission profiles, reliability block diagrams, MTBF or failure-rate analysis, life-data analytics, and confidence intervals.
• Strong proficiency with statistical and modeling packages such as JMP, Minitab, Reliasoft Weibull++, BlockSIM, plus experience creating dashboards with Python or R and SQL and recurring analytical reports using business intelligence tools.
• Experience analyzing product, test, manufacturing, warranty, service, or field data and translating results into engineering or business decisions.
• Strong written and verbal communication skills, with the ability to explain technical results, assumptions, and uncertainty to cross-functional stakeholders.

Nice to have

• Experience with utility-scale power conversion, inverters, renewable energy systems, industrial electronics, or other complex products deployed in large fleets.
• Experience developing warranty cost or life projections, early-launch forecasts, reliability growth models, or field performance dashboards.
• Familiarity with Bayesian methods, accelerated life modeling, reliability allocation, availability modeling, and product genealogy or configuration data.
• Experience with ReliabSoft, JMP, Minitab, PowerBI, Tableau, or comparable tools; Certified Reliability Engineer (CRE) or equivalent certification.

Details

• Location: Hyderabad, Telangana, India.
• Works with Systems, Electrical, Mechanical, Power Electronics, Validation, Quality, Service, Product Management, Finance, and Data teams.