Functional engineer reporting

Job Purpose

Position Overview 

You will join the Data Science team within AkzoNobel’s Digital Center of Excellence, working at the heart of a global manufacturing environment. Your mission: accelerate world-class end-to-end supply chain performance by delivering scalable, secure, and high-impact AI/data products on our Azure cloud platform. 

You’ll collaborate with supply chain leaders (MAKE, SOURCE, DELIVER, QUALITY, PLAN, IBP), IT, engineering, and external software partners. You will operate in a product mindset—turning business needs into AI-enabled capabilities that are reliable in production, measurable in value, and continuously improved.

Job Purpose (What you will deliver) 

On a global scale, you will co-deliver AI and analytics solutions that improve **service, cost, inventory, and planning stability**, including:

1. **E2E Reporting & Decision Intelligence** 

   Modern analytics and KPI frameworks across Integrated Supply Chain (MAKE, SOURCE, DELIVER, QUALITY, PLAN & IBP), enabling faster and better decisions.

2. **Master Data Quality & Automation** 

   Improve and govern master data (lifecycle statuses, MRP parameters, routings, capacity, production data capture) to stabilize planning and execution.

3. **Predictive, Prescriptive & Optimization Models** 

   Build and deploy models on big-data platforms to optimize MRP and network design, such as demand segmentation (ABC/XYZ), supply policies (MTO/MTS), lot sizing, safety stock placement/levels, and capacity-aware planning.

4. **Automation for IBP & Financial Planning** 

   Deliver robust, auditable and scalable global solutions for planning processes with a strong focus on reliability, adoption, and sustainable performance.

5. **AI Enablement & Responsible AI** 

   Introduce and scale AI capabilities (ML, optimization, and GenAI where valuable) with clear governance, model risk management, and ethical/secure implementation.

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Key Activities

You will contribute as **Data Scientist + AI Product Owner** in an Agile/DevOps setup.

### 1) Data Science & Applied AI 

- Design, train, evaluate, and deploy **machine learning, statistical, and optimization** solutions for forecasting, segmentation, anomaly detection, root cause analysis, and decision recommendations. 

- Apply best practices for **feature engineering, model validation, uncertainty quantification**, and error metrics aligned with supply chain decisions. 

- Translate business questions into testable hypotheses and measurable model outcomes (e.g., service level impact, inventory reduction, stability improvements). 

### 2) AI Product Ownership (end-to-end) 

- Define product vision, value proposition, roadmap, and success metrics for AI/data products.- Manage and prioritize the backlog (epics/user stories), align stakeholders, and drive iterative delivery. 

- Ensure adoption: design for user workflows, change management, and training materials. 

- Drive continuous improvement based on feedback, performance monitoring, and business results.

### 3) Data Engineering & Platform Collaboration 

- Work hands-on with data pipelines: extract, cleanse, standardize, validate and document data. 

- Partner closely with Data Engineers/Architects to build robust data products (quality checks, lineage, access controls, reusability). 

- Contribute to scalable patterns for batch/stream processing (where relevant) within Azure.

### 4) MLOps / LLMOps / Production Reliability 

- Operationalize models with CI/CD, automated testing, monitoring, and drift detection. 

- Establish model lifecycle practices: versioning, retraining triggers, model registry, and reproducible pipelines. 

- For GenAI use cases: implement **prompt/version management**, evaluation, guardrails, and secure integration (e.g., retrieval-augmented generation for supply chain knowledge). 

### 5) Data Analytics, Reporting & Storytelling 

- Build decision-grade dashboards and semantic layers for multi-stakeholder reporting (Power BI or similar). 

- Communicate findings clearly to business and technical audiences; create functional/technical documentation. 

### 6) Governance, Security & Responsible AI 

- Ensure solutions comply with data privacy, security standards, and Responsible AI principles (fairness, explainability, traceability, auditability). 

- Define and track **model KPIs** (accuracy, stability, bias checks, latency, uptime, business outcome metrics).

### 7) Team & Culture 

- Mentor colleagues and promote knowledge sharing across data science, analytics, and supply chain communities. 

- Contribute to ways of working (Agile ceremonies, DevOps, documentation standards).

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Experience

## What You Bring 

### Core competencies 

- Proactive, outcome-driven, and comfortable operating in ambiguity. 

- Strong stakeholder management: able to bridge supply chain, IT, and data science. 

- High attention to detail with large/complex datasets and business-critical processes. 

- Strong communication skills in English (written and verbal).

### Functional expertise (AI + Supply Chain) 

- MSc or PhD in Computer Science, Data Science/AI, Machine Learning, Mathematics, Statistics, Industrial Engineering, Supply Chain or similar, with **4+ years** applying AI/analytics in an enterprise setting. 

- Proven end-to-end delivery of **AI/ML solutions** (problem framing → modeling → deployment) for supply chain use cases (e.g., demand forecasting, inventory/service optimization, anomaly detection, ETA/lead-time prediction, segmentation, network/production planning). 

- Strong **supply chain domain experience** across planning and execution (MAKE/SOURCE/DELIVER/QUALITY/PLAN, IBP/S&OP), incl. working with **ERP/APS/MRP** concepts and data (BOM, routings, times, safety stock, lot sizes, capacities) and translating them into scalable data products. 

- Hands-on experience with **cloud + MLOps (preferably Azure)**: CI/CD for ML, model registry/versioning, automated testing, monitoring & drift detection, retraining strategies (Databricks/Spark/MLflow or equivalents); **GenAI/LLM** experience and **Responsible AI** familiarity are strong pluses.

### Technical skills (updated for AI) 

- Strong programming: **Python**, **SQL**, **PySpark/Scala** (or willingness to deepen Scala). 

- Experience with ML libraries and practices (e.g., scikit-learn, statsmodels, Spark ML, MLflow or equivalent). 

- Solid understanding of cloud data platforms; **Azure** experience is a strong plus (e.g., Databricks, Data Lake Synapse, ADF). 

- Experience with DevOps/MLOps concepts: CI/CD, automated testing, monitoring, model/version management. 

- Nice to have: Power BI, Alteryx, R, experience with optimization solvers (e.g., OR-Tools, Gurobi/CPLEX), time-series forecasting. 

- GenAI/LLM experience (nice to have but valued): RAG patterns,, prompt engineering, secure enterprise integration.

### Domain experience 

- Supply chain planning exposure (APS, ERP, IBP), lean manufacturing, or demonstrated supply chain analytics capability is a strong advantage. 

- Understanding of ERP planning & production master data and its impact on MRP and execution. 

- Hands-on experience translating analytics into operational improvements.

## Benefits 

- Hybrid working: **40% home / 60% office** 

- 32–40 hours/week 

- Training: Microsoft Certifications, Language courses, Agile courses 

- Career paths across Data Science, AI Product, and Supply Chain domains

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## Join Us 

If you want to build AI products that materially improve global manufacturing and supply chain performance—reliable in production, measurable in impact, and scalable across regions—join AkzoNobel’s digital transformation journey.