Wood Mackenzie is the global leader in analytics, insights and proprietary data across the entire energy and natural resources landscape.
For over 50 years our work has guided the decisions of the world’s most influential energy producers, utilities companies, financial institutions and governments.
Now, with the world’s energy system more complex and interconnected than ever before, sector-specific views are no longer enough. That’s why we’ve redefined what’s possible with Intelligence Connected.
By fusing our unparalleled proprietary data with the sharpest analytical minds, all supercharged by Synoptic AI, we deliver a clear, interconnected view of the entire value chain. Our trusted team of 2,700 experts across 30 countries breaks siloes and connects industries, markets and regions across the globe.
This empowers our customers to identify risk sooner, spot opportunities faster and recalibrate strategy with confidence – whether planning days, weeks, months or decades ahead.
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Intelligence Connected
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Wood Mackenzie Values
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Inclusive – we succeed together
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Trusting – we choose to trust each other
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Customer committed – we put customers at the heart of our decisions
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Future Focused – we accelerate change
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Curious – we turn knowledge into action
The Opportunity
Global wind and solar capacity additions now exceed all other generation technologies combined, and the accuracy of weather-driven production estimates has become a first-order determinant of asset value, financing cost, and curtailment risk across every region Wood Mackenzie covers. As markets move toward hybrid, storage-paired, and round-the-clock renewable structures and merchant price exposure rises, the gap between a well-specified and a poorly-specified production profile translates directly into basis points on project IRR and into real disputes between developers, lenders, and offtakers.
Wood Mackenzie's Power & Renewables research is the reference point utilities, IPPs, developers, investors, and financial institutions turn to for independent, defensible power market and asset-level views worldwide. That standing rests on the credibility of the data underneath it, and weather-to-power modelling is one of the least commoditised, highest-leverage parts of that data stack. The function is also evolving fast: machine learning and AI-based approaches to weather nowcasting, satellite-derived resource estimation, and calibration are moving from research curiosities to production tools, and there are relatively few analysts who combine meteorological data fluency, power market context, and modern ML tooling, making this a distinctive and fast-growing skill set to build early in a research career.
Joining this function gives you direct exposure to how resource assessment assumptions flow through into capacity expansion outlooks, price curves, and asset valuations used in live client transactions across multiple global markets, rather than working on production forecasting as an isolated technical exercise
The Role
We are seeking an experienced Senior Research Analyst to lead the weather modelling and wind/solar production forecasting methodology for Wood Mackenzie's global Power & Renewables research. You will design and continuously improve our production profile modelling framework, including its calibration and validation processes and its use of satellite data and AI/ML-based techniques, mentor junior analysts, and act as the primary technical point of contact for clients and internal teams on resource assessment and generation forecasting matters across markets.
This role combines deep technical ownership of weather-to-power modelling with client-facing research delivery, requiring both first-principles modelling judgement and the ability to communicate methodology and results to investment committees, developers, and policymakers globally.
Key Responsibilities
Methodology ownership and model development
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Own and evolve the global methodology for converting weather and satellite data into wind and solar production profiles, including power curve selection, wake loss modelling, soiling and degradation assumptions, and curtailment treatment, applied consistently across markets
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Design and implement long-term resource assessment frameworks (P50/P90 exceedance, inter-annual variability, MCP correlation techniques) suitable for bankable, investment-grade output, incorporating asset characteristics (turbine and panel specifications, layout, hub height, tilt/azimuth) into profile generation
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Integrate satellite irradiance, reanalysis (ERA5, MERRA-2), and NWP-based forecast data into a coherent, defensible production profile framework, with clear documentation of assumptions, data lineage, and known limitations
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Evaluate and lead the adoption of machine learning and AI/LLM-based methods for weather nowcasting, power curve calibration, anomaly detection, and forecast automation, assessing where they materially improve on physical or statistical approaches and where they introduce interpretability or reliability risk
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Lead calibration and validation of production profiles against SCADA and grid-metered generation globally, quantifying forecast error by technology and geography, and driving methodology refinements based on observed bias
Forecasting and market integration
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Ensure wind and solar production profiles are correctly integrated into Wood Mackenzie's capacity expansion, dispatch, and price formation models, understanding how profile shape and inter-annual variability propagate through to curtailment, merchant price capture, and asset revenue forecasts across markets
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Incorporate emerging trends, including repowering, hybridisation with storage, and evolving curtailment patterns, into production forecasting assumptions globally
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Track advances in weather modelling, satellite data products, and AI/ML forecasting techniques, and assess their applicability to Wood Mackenzie's commercial modelling stack
Team leadership and quality control
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Lead and mentor a team of Analysts and Research Analysts, reviewing their weather and satellite data processing, model outputs, and documentation for accuracy and consistency.
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Establish and enforce quality control standards across weather data sourcing, profile generation, calibration, and validation workflows, ensuring outputs are reproducible and audit-ready across all markets covered.
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Manage workflows and priorities across global production profile deliverables, ensuring timely, high-quality delivery against research publication and client project deadlines
Client and stakeholder engagement
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Act as the technical lead on resource assessment and production forecasting for client engagements, bespoke advisory projects, and due diligence support for renewable energy transactions worldwide
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Present methodology and findings to clients, industry stakeholders, and internal research teams through reports, presentations, and direct engagement, translating technical modelling detail into commercially relevant conclusions
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Collaborate with regional P&R research and modelling groups to ensure production profile methodology is applied consistently and reflects Wood Mackenzie's global modelling standards
Requirements
Qualification and experience
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7–10 years of relevant experience in wind/solar resource assessment, weather/production forecasting, or renewable energy technical advisory, gained at a developer, IPP, technical due diligence firm, meteorological services provider, consultancy, or financial institution
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Master's degree or equivalent in Meteorology, Atmospheric Science, Renewable Energy Engineering, Electrical Engineering, Data Science, or a related quantitative discipline
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Demonstrated track record of leading resource assessment or production forecasting methodology, including bankable long-term yield assessments used in financing or investment decisions
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Advanced proficiency in Python (pandas, NumPy, xarray, scikit-learn) and/or SQL for large-scale weather and satellite time-series data processing; experience with geospatial and NetCDF data formats
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Direct experience with reanalysis and satellite weather datasets (ERA5, MERRA-2, NSRDB, CAMS, SARAH) and NWP-based forecasting systems; experience with resource assessment or plant performance tools (e.g. PVsyst, SAM, WAsP, WindPRO) preferred
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Hands-on experience applying machine learning or AI/LLM-based methods to weather forecasting, power curve calibration, or data pipeline automation, and a clear-eyed view of where such methods add value versus where physical/engineering approaches remain more defensible
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Experience integrating production profiles into power system or dispatch models (e.g. Plexos, Aurora) is a strong advantage
Knowledge and skills
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Deep understanding of wind and solar plant technology, asset characteristics, losses, and the physical and statistical drivers of capacity factor variability across diverse global geographies and climates
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Strong grasp of how production profile shape and variability feed into curtailment risk, merchant price capture, and asset-level revenue outcomes
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Ability to critically assess modelling assumptions, identify limitations in data or methodology, and recommend more robust or commercially defensible approaches, including when to adopt versus resist AI/ML-based tooling
Communication and leadership skills
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Strong written and verbal communication skills in English, with the ability to present technical methodology to non-technical, senior audiences
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Demonstrated experience mentoring or managing junior analysts
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Fluency in additional languages is an advantage
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Equal Opportunities
We are an equal opportunities employer. This means we are committed to recruiting the best people regardless of their race, colour, religion, age, sex (including pregnancy, sexual orientation, and gender identity), national origin, disability or protected veteran status. You can find out more about your rights under the law at www.eeoc.gov
