Ecozen (www.ecozensolutions.com) is a technology company, and through our solutions, we are building a smart and sustainable future. We believe technology and innovations have the power to bring change and we look to harness this power to build solutions that deliver impact to our customers, our people and our planet.
Our products have revolutionized the irrigation and cold chain industries, with path-breaking innovations in predictive analytics, motor controls, energy storage, AI & IoT modules and food tech. Our technological innovations are now set to disrupt the EV, financial services and asset management industries as well.
We bring these innovations to market fast. How are we so fast? We actively collaborate and trust each other. We listen to our customers and learn fast and unlearn even faster. We predict (create) the future. And most importantly we empower our people with the ability to decide.
As Battery Data Scientist, you would be part of the IoT and Analytics team at Ecozen, which is building state of the art analytics for physical systems including Battery systems for EV and Energy Storage Systems.
Responsibilities:
• Solve battery analytics problems using mix of Physics and data-based approaches
Develop deep understanding of battery physics and develop battery models based on battery physics using
empirical/analytical/physical governing equations utilizing various battery modelling tools and techniques
• Develop techniques to determine required model parameters using Data science, optimization, field data, data from
research as well as dedicated testing as deemed necessary
• Generate synthetic data from physics based models to power and train data driven models as well as to get better insight
into battery performance metrics useful from field data.
• Explore different feature extraction methodologies and performance metrics of batteries under different battery usage
and ageing conditions for discovering insights
• Conduct primary and secondary research for deeper understanding of battery behavior's and build novel modelling
approaches
• Develop new monitoring indicators based on battery physics and their associated algorithms using time series prediction,
regression, and classification and anomaly detection techniques, with the final goal to deploy those using cloud-based
tools
Qualifications:
· Bachelors in CS/Statistics/Electrical Engineering/Mechanical Engineering
· Masters or PhD is preferred with experience (professional or research) in Lithium-ion Battery modelling/simulation
· 3+ year of experience working with Data Science/AI based analytics and/or LIB design/modelling/simulation
Expected Skills:
· Knowledge of Data wrangling, Data cleaning, Data visualization skills using Python
· Prior experience working with large volume time series data and data from real devices
· Working knowledge of Battery models (P2D, ECM) and their implementation in popular tools like Pybamm or others.
· Knowledge in signal processing (Kalman Filters, Particle filters, Optimization algorithms etc.), Statistics, probabilistic machine learning techniques (like Bayesian statistics, RVM etc.)
· Programming skills in Python and working knowledge of deep learning frameworks like TensorFlow, Pytorch, Keras, LSTM, CNN etc
· Background in Electrochemistry and experience in Battery modelling/simulations with sound understanding of Li-Ion battery physics
