Market Modeller

L&T Technology Services; Bengaluru; On-site; INR 900,000 - 1,600,000; Full time

Job summary

L&T Technology Services in Bengaluru is seeking a passionate energy modeller who translates ideas into numbers to enable better decision‑making in evolving markets.

You will build European energy market models, backtest performances, benchmark tools, and report analyses, collaborating with engineering and operations teams.

Qualifications

• Masters degree in a related field required.
• 3–12 years of energy modelling, optimisation or quantitative analysis experience.
• Strong Python for model development, data analysis and automation.
• Ability to translate technical and business requirements into clean, maintainable code.
• Experience handling large time‑series datasets (prices, load, weather, generation, dispatch).
• Strong communication of complex analytical concepts to stakeholders.
• Solid documentation covering models, assumptions and results.

Responsibilities

• Build energy market models for Europe using standard tools.
• Perform backcasting to assess model performance.
• Benchmark various available energy market tools.
• Report generation of the model analysis.

Skills

Energy modelling

Python

Time-series analysis

Data analysis

Optimization

Backcasting

Model deployment

Communication with stakeholders

Documentation

Collaborative teamwork

Education

Masters degree in Power Systems / Energy Markets / Applied Mathematics / Economics or related field

Job description

We are looking for someone who is passionate about translating ideas into numbers that enable better decision-making.

You should enjoy analysing commercial value in the context of complex, uncertain and evolving market conditions.

• Build energy market models for Europe using standard tools.
• Perform backcasting to assess model performance.
• Benchmark various available energy market tools.
• Report generation of the model analysis.

Experience

• 3-12 years of relevant experience in energy modelling, optimisation or quantitative analysis within the power sector.

Education

• Masters degree from a recognized university (Power Systems, Energy Markets, Applied Mathematics, Economics or related field).

Technical Skills

• Proven expertise in energy system, using quantitative and computational methods.
• Hands‑on experience in the modelling, development and deployment of models.
• Strong proficiency in Python for model development, data analysis and automation with the ability to translate technical and business requirements into clean, efficient and maintainable code.
• Strong analytical and problem‑solving skills with the ability to research and learn new techniques.
• Experience working with large time‑series datasets (prices, load, weather, generation, dispatch).
• Ability to communicate complex analytical concepts clearly to technical and non‑technical stakeholders.
• Strong documentation skills covering models, assumptions and analytical results.
• Collaborative team player with a structured and proactive working style.

This role is suited for candidates with a strong quantitative inclination who are excited to work on optimization, modelling and market-driven decision-making in energy systems.

Good to Have Skills

• Develop and maintain energy system and market models that capture the interaction between renewable generation, energy storage, demand, grid constraints and electricity markets.
• Validate, stress‑test and interpret model outputs to ensure results are technically robust, commercially meaningful and aligned with real‑world system behaviour.
• Collaborate with cross‑functional teams (engineering, investment and operation) to develop and refine operating and revenue optimisation strategies for renewable and storage assets.
• Translate complex modelling and analytical outputs into clear insights and recommendations for technical and non‑technical stakeholders.
• Track and analyse developments across electricity markets in Europe, including day‑ahead, real‑time, balancing and ancillary service markets to continuously improve modelling assumptions and optimisation approaches.