Research Analyst

WOOD MACKENZIE INC.; Gurugram District; On-site; INR 1,200,000 - 2,200,000; Full time

Job summary

Wood Mackenzie Inc. is seeking a technically strong Research Analyst to build and maintain weather and production profile datasets that underpin global power market research.

You will work with meteorological, satellite, and asset-level data to generate hourly generation profiles feeding capacity expansion models and revenue forecasts. This hands-on role focuses on data processing, calibration, and QA/QC, with increasing use of machine learning and AI-based methods to improve forecast accuracy.

Qualifications

• 2–5 years of relevant experience in renewable energy resource assessment or meteorology.
• Experience with wind/solar production forecasting and data analysis.
• Strong Python programming and SQL data processing skills.

Responsibilities

• Build and maintain hourly wind and solar production profiles at site and portfolio level.
• Calibrate production profile models against actual generation data; quantify forecast error (MAPE/RMSE).
• Apply ML/AI methods to improve production profile accuracy where appropriate.
• Document methodology, data sources, and validation results for transparency.

Skills

Python programming

SQL data processing

English communication

Education

Bachelor's or Master's degree in Meteorology/Atmospheric Science/ Renewable Energy Engineering/ Environmental Science/ Electrical Engineering/ related quantitative discipline

Tools

Python (pandas, NumPy, xarray)

SQL

Job description

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.Wood MackenzieIntelligence ConnectedWoodMac.comWood Mackenzie Brand VideoWood Mackenzie ValuesInclusive – we succeed togetherTrusting – we choose to trust each otherCustomer committed – we put customers at the heart of our decisionsFuture Focused – we accelerate changeCurious – we turn knowledge into actionThe OpportunityGlobal 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 RoleWe are looking for a technically strong Research Analyst to build and maintain the weather and production profile datasets that underpin Wood Mackenzie's global power market research. You will work closely with Senior Analysts and Principal Analysts across regional teams to translate raw meteorological, satellite, and asset-level data into hourly generation profiles that feed directly into our capacity expansion models, price forecasts, and asset-level revenue models.This is a hands-on, technically-oriented role. You will spend the majority of your time working with weather and satellite datasets, asset characteristics data, calibration and validation workflows, and increasingly with machine learning and AI-based methods, building and QAQCing hourly production profiles and validating model output against observed plant generation across multiple geographies.Key ResponsibilitiesWeather data processing and quality controlSource, clean, and process gridded weather datasets, including reanalysis products (ERA5, MERRA-2), satellite-derived irradiance (e.g. NSRDB, CAMS, SARAH), and numerical weather prediction (NWP) output, for use in production profile modellingPerform quality control on raw weather inputs, including gap-filling, bias correction against ground-station or satellite reference data, and consistency checks across data vintagesMaintain structured weather databases across South Asia and APAC, ensuring version control and traceability of underlying data sourcesWind and solar production profile modellingBuild and maintain hourly wind and solar production profiles at site and portfolio level, incorporating asset characteristics (turbine model and hub height, panel technology and tilt/azimuth, inverter specification, layout and spacing) alongside power curve modelling, wake loss adjustment, system losses, degradation, and curtailment assumptionsSupport long-term resource assessment (P50/P90 exceedance analysis) for utility-scale wind and solar assets globally, applying inter-annual variability and measurement-correlate-predict (MCP) techniques where relevantApply machine learning and statistical methods (e.g. gradient boosting, random forests, neural network-based weather-to-power mapping) to improve production profile accuracy where they outperform physical or engineering-based approaches, under the guidance of senior team membersTranslate site-level weather, satellite, and asset data into generation profiles that feed into Wood Mackenzie's capacity expansion, dispatch, and asset revenue modelsModel validation and back-testingCalibrate production profile models against actual SCADA or grid-metered generation data, and validate forecast accuracy by quantifying and documenting forecast error (MAPE, RMSE) by technology, geography, and time horizonSupport back-testing of forecast methodologies against realised generation, identifying systematic biases (e.g. wake modelling, soiling, curtailment under-estimation) and feeding corrections back into the calibration workflowAssist in evaluating and piloting AI/LLM-assisted tools for data processing, documentation, and anomaly detection within the production forecasting workflowResearch and data supportTrack and interpret weather-driven trends relevant to renewable output across global markets, including seasonal and inter-annual variability, and their implications for capacity factors and revenue forecastsSupport Senior and Principal Analysts with data preparation, chart production, and analysis for client deliverables, research notes, and bespoke advisory projectsMaintain thorough documentation of modelling assumptions, data sources, and methodology to ensure model outputs are transparent, defensible, and repeatable across marketsRequirementsQualification and experience2–5 years of relevant experience in renewable energy resource assessment, meteorology, wind/solar production forecasting, or power system data analysis; experience at a renewable energy developer, IPP, technical advisory firm, or meteorological services provider is an advantageBachelor's or Master's degree in Meteorology, Atmospheric Science, Renewable Energy Engineering, Environmental Science, Electrical Engineering, or a related quantitative disciplineWorking knowledge of wind and/or solar resource assessment methodologies, including power curve application, wake modelling concepts, and irradiance-to-power conversion (e.g. PVsyst, System Advisor Model, WAsP, or equivalent)Proficiency in Python (pandas, NumPy, xarray) and/or SQL for processing large, gridded, time-series weather datasets; familiarity with NetCDF and geospatial data formats is an advantageExposure to reanalysis or satellite weather datasets (ERA5, MERRA-2, NSRDB, CAMS) and NWP model output is highly desirableKnowledge and skillsFoundational understanding of wind and solar generation technology, plant-level losses, and the drivers of capacity factor variability across seasons and geographiesStrong attention to detail and comfort working with large, imperfect, multi-source datasetsAbility to clearly document methodology and communicate technical findings to non-technical audiencesCommunication skillsStrong written and verbal communication skills in EnglishFluency in additional European/ Asian languages is an advantageEqual OpportunitiesWe 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