Vacancy details

AI/ML Engineering

Data Scientist

Senior

Poland, Portugal, Ukraine

Remote

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We are hiring a Senior Data Scientist focused on applied machine learning and surrogate modeling for automotive thermal systems. This is a hands-on PoC role for someone who can challenge assumptions, design the right experiments and determine whether an ML solution is accurate and reliable enough to continue beyond feasibility testing.

What project we have for you

The project will explore whether a fast surrogate model can approximate extended solar-soak simulations and predict cabin temperature at 8 and 10 hours, component temperatures, evaporator and refrigerant temperatures, heat distribution and critical operating conditions. Because only three or four simulation datasets are currently available, the first stage will create a representative dataset through controlled Design of Experiments (DOE) runs covering heat-exchanger variants, pump speeds, temperatures, flow behavior and compressor settings.

The PoC should use deterministic simulation automation before considering an LLM or agentic interface. GT-SUITE already supports DOE, sensitivity analysis, metamodeling, distributed execution and Python-based automation; the Data Scientist will assess these native capabilities alongside simple Python-based surrogate models and recommend the smallest approach that meets the accuracy target.

What you will do

Define the PoC hypothesis, prediction targets, input design space, acceptance metrics and go/no-go criteria with thermal engineers.
Design controlled DOE runs for heat-exchanger, pump-speed, temperature, flow and compressor variations.
Assess whether GT-SUITE’s native DOE, sensitivity-analysis and metamodeling features satisfy the use case before building custom components.
Prepare and analyze simulation outputs, identify influential variables and detect gaps in design-space coverage.
Train and compare simple surrogate-model baselines for cabin, component, evaporator and refrigerant temperatures and heat distribution.
Validate models on unseen operating conditions and quantify prediction error, uncertainty and out-of-range behavior.
Track experiments and model artifacts with MLflow and use Databricks Feature Store where it adds clear value.
Document assumptions, findings, limitations and the evidence behind the final technical recommendation.
Present a clear go/no-go decision and a pragmatic next-step plan, including whether an agentic interface has sufficient value for a later phase.

What you need for this

6+ years of professional data science or applied machine learning experience.
Strong Python skills and solid foundations in statistics, regression, supervised learning and numerical analysis.
Hands-on experience designing experiments and building surrogate, response-surface or predictive models for continuous outputs.
Strong understanding of model validation, error analysis, uncertainty estimation and extrapolation/out-of-distribution risk.
Experience working with small or expensive-to-generate datasets and making evidence-based feasibility recommendations.
Ability to compare simple baselines with more complex approaches and select models based on measured performance rather than novelty.
Practical experience with MLflow or equivalent experiment tracking and model lifecycle tooling.
Ability to collaborate with simulation and thermal engineers and translate physical constraints into experiments, features and validation criteria.
Proactive research and communication skills, including the confidence to recommend a better approach when the original idea is not supported by evidence.
Good spoken and written English.

Nice to have

Experience with GT-SUITE, GT-Automation or comparable CAE/simulation platforms.
Knowledge of DOE sampling, sensitivity analysis, Gaussian-process/interpolation models, polynomial response surfaces or physics-informed ML.
Automotive thermal management, HVAC, heat-transfer or fluid-systems experience.
Experience with Databricks, MLflow on Databricks and Feature Store.
Experience automating batch simulations or working with distributed simulation workloads.

What it’s like to work at Intellias

At Intellias, where technology takes center stage, people always come before processes. By creating a comfortable atmosphere in our team, we empower individuals to unlock their true potential and achieve extraordinary results. That’s why we offer a range of benefits that support your well-being and charge your professional growth.
We are committed to fostering equity, diversity, and inclusion as an equal opportunity employer. All applicants will be considered for employment without discrimination based on race, color, religion, age, gender, nationality, disability, sexual orientation, gender identity or expression, veteran status, or any other characteristic protected by applicable law.
We welcome and celebrate the uniqueness of every individual. Join Intellias for a career where your perspectives and contributions are vital to our shared success.

Skills

Python