Data Scientist

Dear Candidate,

Greetings from AkzoNobel (Experts in the proud craft of making paints and coatings since 1792)

We are hiring for the role of Data Scientist at Pune (Hybrid role), below are additional details as required.

No of Positions - 5

Position No 1 (2 roles) - Skills - Data Science & ML Concepts, Statistical libraries, Python, SQL, Deployments & Azure etc.

Position No 2 (2 roles) - Skills - Descriptive & Prescriptive Analytics, Datalake & Synapse, Data Science & ML Concepts, Statistical libraries, Python, SQL, Deployments & Azure etc.

Position No 3 (1 role) - Skills - Pyspark, Creating Notebooks (Python & SQL), Databricks Dashboarding, DBK Genie, Data Science & ML Concepts, Statistical libraries, Python, SQL, Deployments & Azure etc.

Overview

You will join the AkzoNobels Advanced Analytics team, 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.

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

Job Purpose

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

• E2E Reporting & Decision Intelligence**

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

• Master Data Quality & Automation**

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

• 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.

• Automation for IBP & Financial Planning**

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

• 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.

Role & Responsibilities

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 **skills/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).

Regards,

Team AkzoNobel