BMS Algorithm Data Engineer for Battery Analytics

Tata Technologies Limited; Stratford-upon-Avon; On-site; GBP 60,000 - 90,000; Full time

Benefits offered by this job

Pension Scheme

Tata Jaguar Land Rover PrivilegeScheme

Health Assured

Health Shield

Job summary

Tata Technologies Limited is seeking a Data Analysis Engineer to lead data analysis and development of BMS algorithms at the Gaydon site. The role sits within the Embedded Systems Solutions department, collaborating across software, hardware, and compliance teams to deliver robust battery management solutions.

You will analyze field data, validate models with statistical methods, and apply machine learning to improve anomaly detection and predictive maintenance.

Qualifications

• Bachelor's or Master's degree in Electrical Engineering, Computer Science, Applied Mathematics or related field.
• Strong knowledge of battery management systems (BMS).
• Proficient in Python and MATLAB for modeling and data analysis.
• Experience with battery systems, embedded control algorithms, or energy storage technologies.

Responsibilities

• Design, implement, and maintain control frameworks for battery algorithm development.
• Collaborate with audit, finance, and engineering teams to support documentation and testing of controls.
• Analyze high‑resolution battery data from field units and test environments to inform algorithm performance.
• Interface with software, hardware, and compliance teams to drive integrated product development.

Skills

BMS

Python

MATLAB

Data analysis

Machine learning concepts

Education

Bachelor's or Master's degree in Electrical Engineering, Computer Science, or Applied Mathematics

Tools

MATLAB/Simulink

Job description

Tata Technologies Limited is seeking a Data Analysis Engineer to lead data analysis and development of BMS algorithms at the Gaydon site. The role sits within the Embedded Systems Solutions department, collaborating across software, hardware, and compliance teams to deliver robust battery management solutions.

You will analyze field data, validate models with statistical methods, and apply machine learning to improve anomaly detection and predictive maintenance.