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Specialized battery ML role reduces competition, but mid-level Bangalore posting increases candidate pool.
High domain specificity for battery energy storage reduces cross-industry transferability.
Mandated 3–7 years, production ML, battery domain and engineering collaboration impose strict filters.
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Develop and maintain advanced data-driven models and analytics for Battery Energy Storage Systems (BESS) performance assessment and control, including forecasting, anomaly detection, and predictive maintenance.
Create visualizations and reports to communicate model results, KPIs, and key insights to stakeholders.
Collaborate with product managers, data scientists, and software engineers to integrate analytics solutions into Fluence’s platforms.
Bachelor’s degree in Data Science, Computer Science, Electrical/Mechanical/Chemical Engineering, Mathematics, or related field.
3 to 7+ years of professional experience in data science, software engineering, controls, or related roles preferably in energy sector/battery storage/renewable energy.
Proficiency in Python with experience in scientific libraries (NumPy, Pandas, Scikit-learn, TensorFlow, Pyomo, Plotly).
Experience developing scalable, production-grade analytics solutions in collaboration with software engineering teams.
Experienced in battery storage or renewable energy analytics with a strong foundation in ML/AI and mathematical optimization.
Capable of independently managing projects and delivering high-quality solutions with minimal supervision.
Skilled collaborator comfortable working cross-functionally between data science, product, and engineering teams.