2.0 - 5.0 years

0 Lacs

bengaluru, karnataka, india

On-site

Battery Data Scientist (Data meets degradation, and intelligence meets impact) Location : Bangalore (On-site) Experience : 2 to 5 years Education : Masters/PhD Degree in Data Science/Engineering Industry : EV / Batteries / Hardware Startups Reports To : Founder & Head of Product Type : Full-time About Fawkes Energy Fawkes Energy is building intelligent, software-driven platforms to bring safety, transparency, and performance optimization to the EV and battery ecosystem. We are a mission-driven team working toward a sustainable circular economy in mobility and energy storage. Role Overview At Fawkes Energy, were building the future of battery intelligence, repurposing, and lifecycle intelligence. Our tools are a window into the battery pack, and decode what really matters - performance, health, lifetime, and safety. Now, were looking for a Battery Data Scientist who loves wrestling with messy field data, modeling the intricacies of battery degradation, and building intelligence that actually makes a difference. What You&aposll Work On Own projects that involve modeling battery degradation, SoH and lifetime prediction using both traditional and ML-based approaches Develop, validate, and deploy battery degradation models using machine learning, deep learning, and physics-informed methods Work with both internal and external teams to design robust data ingestion and preprocessing pipelines for heterogeneous datasets, from structured lab test data to noisy field-collected logs Work with real-world battery pack field data and cell testing data from our labs Design and build data pipelines, experiment with ML/DL techniques, and validate models across chemistries and use-cases Collaborate with test engineers to drive new experiments and extract meaningful features from cycling, calendar, and abuse tests Guide junior data scientists as the team grows and evolve into a product leadership role Who You Are A PhD in data science, applied ML, electrochemistry, mechanical/electrical engineering or similar (fresh grads welcome!) OR a Masters degree with 25 years of strong hands-on experience in battery or energy analytics Comfortable navigating both structured test datasets and unstructured real-world messiness Solid Python skills; comfort with tools like TensorFlow, PyTorch, Polars. Experience with PINNs is an advantage Experienced in building end-to-end ML workflows from data wrangling and feature engineering to training, validation, and deployment Curious about how batteries age, fail, and behave across chemistries, duty cycles, and environments Bonus: Prior exposure to battery modeling, cell cycling, EVs, BMS data, or time-series data diagnostics What It&aposs Like in Fawkes Energy Fast-paced, impact-driven work environment where youll see your work deployed in real-world EV and battery contexts Small, multi-functional team working at the bleeding edge of clean-tech - hardware, software, and AI under one roof Youll work alongside data analysts, embedded engineers, test engineers, product teams and the founders (who also code and create physics-based models!) You wont just build dashboards, youll shape the core IP that powers diagnostics and decision-making in the battery world Why Join Us Work on a cutting-edge hardware product at the intersection of EV, battery analytics, and climate action Opportunity to lead development and define a new category in the circular economy for EVs Fast-track to a technical leadership role in a high-impact, mission-driven startup Collaborative and technical team environment with real ownership What You Will Get Competitive compensation, health insurance, conference budgets, access to EVs, batteries, and testbeds for experimentation Flexible hours - we are result-first and not hours-clocked A chance to work with batteries that dont sit in simulations, but in vehicles, fleets, and energy systems The opportunity to build core IP at the intersection of hardware, AI, and battery science If this sounds like your kind of gig, email us at [HIDDEN TEXT]. Show more Show less

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