Vacant job

In this role, you will contribute to building next-generation capabilities for assessing battery health and lifetime, enabling smarter decisions across the lifecycle of electric vehicles—from first use to second-life applications.

You will work closely with experts in data science, software, and engineering, bringing critical domain knowledge to ensure that battery insights are technically sound, interpretable, and actionable.

What You Will Do

Develop and validate methods for State of Health (SOH) and Remaining Useful Life (RUL) estimation for Li-ion batteries.

Analyze real-world vehicle and battery data to understand degradation behavior over time.

Interpret and explain battery aging based on factors such as:

• Temperature
• State of Charge (SOC)
• Depth of Discharge (DoD)
• Charging patterns and C-rate

Load profiles and usage conditions.

Compare and evaluate on-board BMS health estimates vs. off-board analytical models.

Define confidence levels, limitations, and applicability of lifetime predictions.

Collaborate with cross-functional teams to ensure models are physically grounded and credible.

Translate battery health insights into practical recommendations for:

• Lifecycle planning
• Residual value and risk assessment
• Warranty and service strategies
• Second-life and reuse scenarios

What We Are Looking For

Strong background in Li-ion battery technology and degradation mechanisms.

Experience with battery lifetime assessment, including:

• Calendar aging
• Cycle aging
• Real-world usage effects

Deep understanding of:

• SOH and RUL
• Capacity fade and resistance increase
• Performance degradation in field conditions

Experience working with battery data from vehicles, BMS systems, or testing environments.

Ability to critically evaluate models and challenge assumptions.

Experience collaborating with data scientists, engineers, and business stakeholders.

Strong communication skills—able to explain complex battery behavior clearly.

Meriting Experience

Experience with battery systems in electric vehicles (BEVs).

Exposure to second-life, residual value, or warranty risk analysis.

Experience comparing BMS estimates with external models.

Basic ability to work with Python, SQL, or similar tools.

Background in high-utilization or commercial vehicle applications.

Education

Degree in electrochemistry, battery technology, electrical engineering, materials science, or similar.

Advanced studies within energy storage systems are a plus.

Why Multiply

Work on real-world electrification challenges with direct impact on sustainability and lifecycle efficiency.

Combine deep engineering knowledge with data-driven decision-making.

Collaborate in cross-functional teams spanning software, data, and systems engineering.

Contribute to building scalable capabilities for next-life battery use and optimization.