Vacancies AI-ML Researcher

The Technology Integration Team develops and productizes reusable Color Technology Components that power AkzoNobels internal applications and external customer-facing tools with focus areas in Colorimetric Engines/Building Blocks Color Visualization and AI/ML-enabled color services. The AI/ML Researcher owns the delivery of applied Machine Learning and statistical solutions for prioritized color and coatings use cases (e.g. formulation optimisation pigment/raw material screening quality prediction and image-based inspection). The role translates business and R&D questions into well-structured datasets validated ML models and clear insights and partners with engineering and product stakeholders to ensure solutions are deployment-ready monitored documented and usable in products and processes. Unlike generalist Researcher roles this position brings dedicated applied ML/data science capability to the team owning the end-to-end ML lifecycle from problem framing through deployment-ready model delivery.

• Use case shaping & stakeholder partnering

o Engage with R&D lab teams manufacturing and product/application stakeholders to translate needs into well-defined ML problems required inputs/outputs constraints and measurable success criteria.

o Lead or co-lead end-to-end delivery for assigned use cases from discovery and scoping through experimentation validation and implementation readiness.

o Communicate trade-offs (accuracy vs. explainability latency cost robustness) and guide stakeholders toward fit-for-purpose solutions.

• Data preparation analysis & modelling

o Acquire clean integrate and analyze data from R&D/lab systems manufacturing sources and internal datasets; assess data quality bias leakage risk and suitability for modelling.

o Develop benchmark and validate ML models (supervised/unsupervised) including baseline creation feature engineering model selection hyperparameter tuning and robustness checks.

• Domain application (color/coatings)

o Collaborate with color formulators researchers and lab teams to ensure modelling aligns with domain realities (measurement variability protocols material constraints).

o Define and drive dataset design/generation activities (e.g. DoE support sampling strategy labeling guidance protocol awareness) to improve training data quality and coverage.

o Apply practical understanding of relevant color data types (spectral/Lab*/ΔE and variability) when designing targets features and evaluation strategies (as applicable).

• Computer vision (where relevant)

o Deliver image-based modelling tasks (e.g. defect detection surface uniformity pigment recognition) using classical CV and deep learning methods; define evaluation metrics and acceptance criteria aligned to operational deployment.

o Ensure peer review and alignment with Senior Researchers/architects for high impact use cases and model choices that may affect product/process behavior.

Industrialization & production readiness

o Drive implementation readiness of assigned models: reproducible training/inference code model artifacts data dependencies parameterization testing approach and clear runbooks.

o Contribute to MLOps practices as defined by the team: version control experiment tracking model registry inputs monitoring requirements alert thresholds and retraining triggers.

o Partner with application/software teams to integrate model outputs into components/services (APIs batch pipelines or embedded usage) and support post-deployment stabilization.

Educational qualifications

o Masters degree (preferred) or Bachelors degree in Data Science Computer Science Statistics Applied Mathematics Operational Research Engineering or related quantitative field.

Relevant experience

o Typically 5-7 years of experience applied machine learning / data science delivering solutions in industrial manufacturing R&D engineering or equivalent complex environments (or equivalent project portfolio demonstrating end-to-end ownership).

o Experience collaborating with cross-functional stakeholders (research engineering product) and delivering work in an agile/team setting.

Required skills

o Strong Python skills for data analysis and ML (pandas NumPy scikit-learn; TensorFlow/ PyTorch beneficial).

o Solid understanding of ML fundamentals (regression classification clustering model validation overfitting control) and statistical reasoning.

o Working knowledge of SQL and working with structured datasets; o Familiarity with data pipelines or cloud platforms (Azure/Databricks) is a plus.

o Computer vision knowledge (OpenCV CNNs/segmentation) is a plus depending on scope.

o Familiarity with Git and reproducible workflows; exposure to MLflow/ experiment tracking is desirable.

o Strong communication skills: ability to present findings clearly and document assumptions/limitations.

o Domain familiarity with color science (CIE color spaces spectral data ΔE) is advantageous but not mandatory.