Market, Technology & Policy Analyst, Energy Transition

Overview

Join S&P Global Energy's team on the energy transition, using data analytics, integration and visualization to produce analysis that supports critical business decisions.

What you'll do

• Aggregate and manage diverse datasets, keeping them consistent, traceable and well documented.
• Safeguard data quality through validation, cross-checking and triangulation.
• Produce clear, high-impact visualizations with transparent assumptions and reproducible logic.
• Build and automate data pipelines and content-generation workflows where possible, so outputs scale.
• Connect datasets across markets, technologies and value chains to surface emerging patterns, gaps and implications.
• Formulate short, fast and impactful analysis of recent trends and how they affect the wider energy system.
• Support fast-turnaround outputs with reliable, scalable data inputs, working across analyst, data and product teams worldwide.

What you'll need

• 3+ years of experience in renewable energy, energy storage, power markets, market research, consulting, or a related industry.
• Prior exposure to energy markets or infrastructure, gained through education or work experience.
• A solid grasp of energy transition dynamics.
• Degree in a quantitative field (e.g. economics, engineering, data science).
• Strong working knowledge of programming and data tools for data extraction, transformation and automation.
• A strong grasp of visualisation principles (clarity, consistency, comparability).
• Structured and detail-oriented, keeping shared datasets and outputs clean.
• Clear, pragmatic communicator who takes feedback constructively.
• Works independently while building strong relationships across a global team of analysts, data and product colleagues.

Nice to have

• Familiarity with Snowflake, Databricks or similar data platforms.
• Experience improving workflows, automation or data processes.
• AI fluency: understands what AI can and cannot do, treats AI outputs as hypotheses to test through triangulation and dataset checks, and knows that designing the full task context (data, assumptions, constraints, sources) matters more than prompting alone.
• Systems thinking: connects signals across markets, technologies and value chains to find patterns and gaps rather than just describing trends.
• Takes ownership of decisions and accountability for outputs; AI supports the analysis, but responsibility stays with you.
• Recognises the limits of analysis and tools, and tells plausible outputs from correct ones.
• Works across teams to source data, align assumptions and improve outputs.

Details

• Location: Gurugram, Haryana, India.
• Grade Level (for internal use): 09.