Senior Battery Data Scientist

Do you want to join a fast-growing climate tech startup with real impact?

About Coulomb

At Coulomb AI, we envision a future powered by the seamless integration of AI and Battery Technologies, creating a powerful synergy to combat climate change. We build category-leading software that can significantly improve battery lifespan and performance by employing advanced predictive analytics, real-time monitoring, and adaptive battery management algorithms.

Coulomb is headquartered in San Francisco with its development and research center in Bangalore. Founders Khushboo Shrivastava and Santanu Mondal are alumni of IIT Bombay and have first-hand experience working on first-generation Electric Vehicles at General Motors. We are backed by some of the best climate tech investors in the world, including Y Combinator, Harvard Management Seed Capital, CSVE Ventures, and prominent Silicon Valley-based Angels.

We already have a global presence with customers in 5 countries across UK, US, APAC and India

Yet, we’re still at the start of our journey, and we want exceptional people to join us!

About the Role

We’re hiring a Battery Data Scientist who thrives at the intersection of electrochemistry, machine learning, and large-scale data systems. You’ll develop scalable models that interpret and predict battery behavior under real-world conditions—feeding our cloud-based digital twin engine.

This is a critical role with full ownership of models from research through deployment in production environments.

As a Battery Data Scientist , here’s what you’ll be doing

• Develop and refine ML-based SOH and RUL prediction models
• Build physics-informed digital twins that simulate real world battery behaviour
• Design and validate ML models for anomaly detection and failure prediction
• Deploy ML models at scale in real-time and batch inference workflows using AWS or equivalent platforms.
• Collaborate with software and data engineers to operationalize models in our battery digital twin platform.
• Continuously monitor and retrain models using new fleet data, teardown results, and feedback from customers.

We are excited about you because you have:

• 5+ years of experience in data science or ML engineering, including experience with battery or energy systems
• Solid understanding of Li-ion battery electrochemistry and real-world degradation modes
• Proficient in Python and tools like NumPy, Pandas, Scikit-learn
• Experience building and deploying ML models at scale, including batch and real-time inference pipelines.
• Familiarity with ML lifecycle tools like MLflow, DVC, Airflow, or equivalent
• Experience working with large-scale time-series data (e.g., telemetry, sensor logs, test bench results)
• Strong data engineering fundamentals; comfortable working with structured/unstructured data on cloud platforms (AWS/GCP/Azure)

You’ll be a great fit if:

• Experience with digital twin systems, battery degradation modeling, or tools like PyBaMM or COMSOL
• Exposure to lab data (Arbin, Maccor, Neware) and teardown-based performance validation
• Familiarity with vehicle telemetry systems (CAN, BMS logs, OBD-II)
• Previously worked in a startup environment and have a high degree of ownership and appetite for ambiguity
• Like being a part of a high-performing team focussed on fast-paced execution at the same time detail-oriented
• A relentless desire to solve customer problems and chase down answers

The number and format of interviews that you'll go through will be personalized to your skillset and the type of role that you're looking for. We want to give everyone the best chance at representing their abilities. Everyone starts with one or two interviews on a video call, and if we think there's a fit, we'll bring you onsite for a round of in-person interviews. Below is the sequence of the process.

1. First Video Call - You should be prepared to talk about your background, experiences, and interest in Coulomb
2. Writing Good Code - The ability to write code that is Efficient, Effective, Easy to read, maintain, and upgrade
3. Analyzing the Efficiency of Code - The ability to analyze and improve the efficiency of code so that it runs with optimal complexity, creating systems that store and transmit data efficiently
4. Technical Interview - You should be prepared to elaborate on your conceptual and practical experience to our Technical experts
5. Culture Fit - Make sure we are aligned to work together