Company culture :

Job :

Design, develop, and train an AI model based on historical data from automotive thermal validation tests to predict key performance indicators (KPIs) for future vehicle lines. The ultimate goal is to reduce or eliminate physical testing, accelerate time-to-market, and optimize resource allocation

Main Responsibilities:

• Data Collection & Processing:

Gather, clean, and structure historical test data from thermal validation campaigns (e.g., climatic chamber tests, endurance tests).
Ensure data consistency and quality from multiple sources (physical tests, benches, in-vehicle sensors, etc.).

• AI / ML Model Development:

Define the architecture of an AI model (e.g., deep learning, machine learning, hybrid models) dedicated to KPI prediction.
Train the model using historical datasets, including:
Coolant circuits (battery, e-motor, HVAC, heat losses)
Refrigerant circuits (pressure drop, IHX)
Component thermal behavior (battery cells, power electronics, cabin, etc.)
Heat exchangers, pumps, valves, actuators
Interaction with ambient conditions and driving cycles

• Analysis & KPI Prediction:

Identify correlations between physical parameters and the target KPIs.
Generate predictions for new vehicle lines with no physical validation.
Benchmark AI predictions against historical physical test results to validate the model.

• Documentation & Reporting:

Document the modeling process, assumptions, and results.
Present findings and recommendations to key stakeholders (validation teams, design teams, quality, management).

Required profile :

• Education:

Master’s degree (or equivalent) in:

Data Science / Artificial Intelligence

Mechanical / Thermal / Energy Engineering

Automotive or Embedded Systems Engineering

• Experience:

More than 1 year of experience in data science or AI applied to complex technical systems.

Experience in the automotive sector or thermal validation is a strong advantage.

• Technical Skills:

Data Science & AI:

Proficiency in Python (Pandas, Scikit-learn, TensorFlow or PyTorch)

Knowledge of regression, neural networks, supervised learning models

Experience with time-series data and predictive modeling

Automotive Thermal Systems:

Understanding of vehicle thermal systems (cooling loops, HVAC, electrified components)

Familiarity with physical testing procedures and KPIs related to thermal performance

Other Tools:

Data visualization tools (Matplotlib, Plotly, Power BI, etc.)

Ability to communicate and document technical work in English and French

Strong analytical mindset and autonomy

• Soft Skills:

Technological curiosity and innovation mindset

Team player with the ability to collaborate across functions (data science, validation, design, calibration)

Strong problem-solving and critical thinking skills

Results-oriented with a focus on optimization and efficiency