Data Scientist

Job Purpose

The Data & Analytics team is focused on unlocking the value of data as it exists within Akzonobel  systems across different towers. We're an eclectic team of data-oriented doers who love to take on new challenges, are always learning, and looking for ways to improve decision-making.

POSITION OVERVIEW:

The Data Scientist is responsible for designing, developing, and deploying advanced analytics and machine learning solutions, including GenAI and LLM-based use cases, that create measurable business value for AkzoNobel. The role combines strong hands-on technical expertise (with a focus on Azure Databricks and Azure ecosystem) and exposure to Large Language Models (LLMs), with solid stakeholder management.

Key Activities

1. Advanced Analytics & Machine Learning

• Translate business problems into data science use cases and define appropriate analytical approaches.
• Design, build, and validate statistical and ML models (e.g., forecasting, classification, optimization, recommendation).
• Develop and maintain production-grade models with appropriate monitoring, retraining, and governance.

2. GenAI & LLM Solutions

• Explore and design solutions using Large Language Models (LLMs) and other GenAI capabilities

Ensure responsible AI use, with attention to data privacy, model security, and ethical use of GenAI.

3. Data Engineering, Azure & Databricks

• Use Azure Databricks (Spark, notebooks, jobs) to perform large-scale data processing, feature engineering, and model training.
• Work with data engineers to design and optimize data pipelines, data models, and lakehouse structures on Azure.
• Leverage Azure services (e.g., Data Lake, Synapse, Data Factory, Azure Machine Learning) where applicable.

4. Stakeholder Management & Business Partnering

• Engage with business stakeholders to understand objectives, define success criteria, and prioritize use cases based on value and feasibility.
• Communicate complex analytical outcomes and trade-offs in clear, business-friendly language.
• Support change management, adoption, and value realization for analytics and GenAI solutions.

5. Project Delivery & Ways of Working

• Lead or co-lead end-to-end data science projects: from ideation, scoping and estimation through development, testing, deployment, and post-go-live monitoring.
• Work in an agile setup, collaborating closely with Product Owners, Business SMEs, Data Engineers, and other IT teams.
• Ensure proper documentation of models, data flows, assumptions, and decisions.

Experience

Technical

• Strong understanding of statistics, machine learning algorithms, and model evaluation techniques.
• Experience with data preparation, feature engineering, and dealing with large, complex datasets.
• Understanding of MLOps concepts: version control, CI/CD for ML, monitoring, and retraining strategies.
• Good understanding of GenAI/LLM concepts (prompting, fine-tuning, grounding with enterprise data, safeguards).

Professional & Interpersonal

• Strong analytical, problem-solving, and conceptual thinking skills.
• Ability to structure ambiguous problems and work independently where needed.
• Effective communication skills; able to engage both technical and non-technical stakeholders.
• Collaborative mindset and ability to work in cross-functional, virtual teams.
• Continuous learning mindset and curiosity about new data, ML, and GenAI technologies.

Behavioral Competencies

• Customer Focus: Understands internal customer needs and focuses on delivering value.
• Drive for Results: Delivers high-quality outcomes within agreed timelines and constraints.
• Collaboration: Builds strong relationships across functions and geographies.
• Innovation: Experiments with new methods and tools, especially around GenAI and advanced analytics.
• Integrity & Compliance: Acts in line with AkzoNobel values and adheres to data privacy, security, and responsible AI guidelines.

Measures of Success

• Business impact delivered (e.g., cost savings, revenue uplift, process efficiency, risk reduction).
• Adoption and sustained usage of analytics and GenAI solutions by business stakeholders.
• Model performance and stability in production, including responsible AI metrics where relevant.
• Feedback from stakeholders and team members on collaboration and communication.
• Contribution to team capability building and reusable assets.

Key Competencies

• Master’s/bachelor’s degree in data science, Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field.
• 5–7 years of hands-on experience in data science / advanced analytics roles.
• Strong proficiency in Python and SQL.
• Solid, practical experience with Azure Databricks (Spark-based data processing, notebooks, workflows/jobs).
• Experience with machine learning libraries and frameworks such as scikit-learn, TensorFlow, PyTorch, or similar.
• Exposure to Large Language Models (LLMs) and NLP techniques, ideally using platforms such as Azure OpenAI, OpenAI, or Hugging Face for building text analytics or conversational solutions.
• Experience on a major cloud platform (preferably Microsoft Azure) and familiarity with data/ML services (e.g., Azure Data Lake, Synapse, Data Factory, Azure Machine Learning).
• Proven track record of delivering and deploying ML solutions into production with measurable business outcomes.