Principal Engineer, Solar PV Tech Assessment

Role Responsibilities

• Own and evolve the company’s production modeling methodology for utility-scale solar, incorporating advances in satellite irradiance datasets, loss factor characterization, and inter-annual variability treatment
• Establish and maintain a living set of internal best-practice standards for PVSyst (or equivalent tool) configuration — covering horizon shading, bifacial gain, soiling profiles, and DC/AC loss assumptions — ensuring consistent, auditable, and defensible outputs across the portfolio
• Develop internally consistent uncertainty frameworks that account for resource variability, model uncertainty, and equipment performance risk in a way that satisfies both lender due diligence requirements and internal investment decision-making
• Serve as the primary internal technical resource for independent engineer (IE) engagement during project financing and tax equity funding, supporting responses to technical findings and defending design assumptions with analytical rigor
• Support MET station campaign design and quality assurance during construction, ensuring that sensor placement, instrument selection, and data validation practices will support bankable long-term performance assessment
• Proactively assess the energy production impact of design changes that arise mid-construction — equipment substitutions, layout modifications, or scope changes — and develop analytical frameworks to quantify and mitigate change-order risk before it materializes
• Build and maintain physics-based simulation models for operating assets that go beyond P50/P90 benchmarking — enabling energy potential estimation under variable conditions, loss attribution, and degradation tracking grounded in first-principles performance modeling
• Collaborate with asset management and O&M teams to translate operational data into modeling feedback loops, improving pre-COD assumptions and creating a more accurate picture of realized versus predicted performance across the fleet
• Collaborate with technology and data teams to ensure that AI-driven outputs are grounded in physical models and do not introduce opaque or unvalidatable assumptions into financeable deliverables
• Provide expert guidance and review for senior engineers and design leads on design development, elevating the overall quality of modeling work without carrying direct people management responsibility
• Maintain awareness of developments in PV simulation research, industry benchmarking (e.g., PVPS Task 13), and regulatory shifts that affect performance modeling standards, and translate relevant findings into actionable updates to internal practice

Job Requirements and Experiences

• A Bachelor’s or Master’s degree in Electrical Engineering, Physics, or a closely related engineering discipline is required
• A minimum of 12 years of progressively senior experience in solar PV engineering, with a material portion focused on utility-scale (100 MW+) projects in the US market is required
• Deep, hands-on expertise in PVSyst, including configuration of bifacial models, near shading scenes, horizon profiles, and inter-annual variability treatment; working familiarity with alternative or complementary platforms (e.g., SAM, Solargis, NSRDB-based workflows)
• Demonstrated experience producing and defending bankable energy yield assessments, including direct engagement with independent engineers during project financing
• Substantive experience with energy uncertainty modeling — whether through internal probabilistic tools or platforms such as Power UQ — and the ability to explain uncertainty outputs to non-technical financial audiences
• Working knowledge of PV system physics — irradiance transposition, temperature coefficients, bifacial gain mechanics, inverter clipping, and DC degradation — sufficient to identify errors in models and outputs that tools do not flag automatically
• Familiarity with MET station infrastructure design — including pyranometer selection, soiling station placement, and data quality validation — particularly in a construction oversight or owner’s engineer context
• Exposure to machine learning or statistical modeling approaches applied to solar performance — irradiance interpolation, soiling estimation, or anomaly detection — and a clear-eyed view of where these methods add value versus introduce risk
• Experience in an asset management or operational analytics role that involved reconciling predicted versus actual performance, or developing monitoring-based loss attribution frameworks
• Experience with capacity testing protocols (e.g., ASME PTC 13 or equivalent) and post-construction performance reconciliation
• Participation in industry working groups, IEA PVPS activities, or similar forums where modeling standards are shaped