Key Responsibilities
- Define the reliability roadmap and targets for stack life, degradation rate and availability, grounded in electrochemical first-principles, spearhead Design for Reliability (DfR) and Manufacturability (DfM) across all stack generations.
- Make DFMEA/PFMEA primary tools (not afterthoughts), linking each failure mode to a test or design control, quantify all reliability claims with documented analytical basis.
- Design accelerated life testing (ALT) protocols and oversee the DVP&R to validate stack components under varied transient loads, pressures and temperatures.
- Implement stack diagnostics (EIS, Cyclic Voltammetry) to monitor decay and find root causes, while respecting the limits of each characterization tool and avoiding over-interpretation.
- Lead structured technical discussions, own root cause analysis on test data from the Testing Director and translate field feedback into design changes with closed-loop tracking.
- Collaborate with Materials Science on membrane, catalyst, and GDL resilience and with Manufacturing/Supply Chain on FAI and IQC metrics tied to reliability.
- Establish a regular communication cadence (technical deep-dives, cross-functional syncs, DFMEA/PFMEA reviews), communicate decisions in writing with supporting data and specific timelines—never vague terms like soon or ASAP.
- Own your full domain—roadmap, qualification plans, design changes, timelines, staffing and budgets—with transparency on both wins and setbacks, escalate early with clear context.
- Set realistic timelines with built-in contingency, plan alternate approaches rather than assuming success and document timeline decisions and their reasoning.
- Integrate cost and lifecycle economics into design decisions, including component-level cost breakdowns and sensitivity analyses, so $/reliability metrics carry unit-cost context.
Qualifications
- B.Tech/M.Tech in Chemical, Materials Science, Metallurgy, or Electrochemistry with a minimum 15+ years of experience in electrolyzer stack development, batteries or fuel cell development.
- Deep knowledge in electrochemical characterization techniques (CV, LSV, EIS), electrochemical degradation mechanisms, safety protocols, and regulatory compliance for electrolyzer/batteries.
- Proven ability to bridge engineering and operational teams to drive measurable product improvements.
- Strong data analysis skills, proficiency with reliability tools and statistical software (Minitab, JMP, or equivalent).
- Exceptional communication, leadership, and organizational skills.
- Understand the limits of diagnostic and characterization tools (EIS, CV, electron microscopy, etc.). Know what each tool can and cannot reliably tell you. Avoid over-interpreting results beyond their technical scope.
Preferred Qualifications
- Track record of systematizing reliability processes and driving measurable improvements across teams.
- Evidence of teaching others scientific reasoning and hypothesis-driven engineering, not just managing tasks.
- Extensive experience with DFMEA and PFMEA methodologies applied to design and manufacturing processes.
- Field experience with deployed electrochemical systems and real-world degradation analysis.
- Familiarity with renewable energy or electrochemical energy storage systems.
Leadership Philosophy & Cultural Fit
This role requires comfort with:
- Quantified decision-making: decisions backed by data, not opinions.
- Structured communication: technical rigor in staff meetings, written rationale for design changes.
- Ownership accountability: transparent reporting of both progress and setbacks.
- Avoiding hope-based planning: planning contingencies, escalating early, not assuming success.
- Continuous scientific learning: staying current on electrochemistry, degradation mechanisms, and characterization techniques.
This role will not be successful if the candidate:
- Prefers best effort over systematic, accountable delivery.
- Communicates vaguely on timelines (soon, ASAP, next week instead of specific dates).
- Treats technical deep dives as status meetings.
- Avoids escalating problems until they become critical.
- Resists making decisions with incomplete information (needs perfect data, not good enough).
- Defaults to trial-and-error approaches instead of hypothesis-driven, first-principles engineering.
