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Niche optimisation skills reduce applicants, but mid-level experience and Pune location increase competition.
Skills in optimisation and ML are transferable, but energy-market expertise raises domain specificity.
Explicit years, degree options, and mandatory optimisation/Python/ML/AWS skills create strict technical filters.
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Develop and maintain Python-based mathematical optimisation models for simulating energy asset behaviour and revenue potential in US wholesale electricity markets.
Build and sustain data pipelines and software components focused on energy asset revenue analysis in the CoMETS SaaS platform.
Ensure high quality code, conduct technical testing, and maintain technical documentation for optimisation models and related software.
Bachelor's degree with 4+ years relevant experience or Master's degree with 2+ years in Electrical Engineering, Computer Science, Applied Mathematics, Operations Research, or related fields.
Expert-level Python coding skills producing clean, well-structured, production-quality code.
Expert-level knowledge of mathematical optimisation tools such as Pyomo, GurobiPy, or LP/MIP solvers.
Work Experience Required: Minimum 2 years with Master’s or 4 years with Bachelor’s degree in relevant fields.
Experienced in building and deploying machine learning models integrated with optimisation workflows.
Familiar and comfortable with cloud services on AWS and using AI-powered developer tools such as GitHub Copilot, ChatGPT, or similar.
Background or strong interest in electricity markets, energy assets (e.g., BESS, Solar, Wind), or power systems, to effectively translate domain-specific needs into optimisation solutions.