Data Scientist – AI Solutions & Generative AI

About the role

We are looking for a Data Scientist who blends strong analytical and modeling expertise with hands‑on AI solution development. You will translate business challenges into actionable AI/ML use cases, prototype solutions quickly, and support their deployment into production environments.

Key responsibilities

• Apply statistical, data‑mining, algorithmic and visualization techniques to model complex problems and deliver actionable insights.
• Build and operationalise machine‑learning models (classification, forecasting, anomaly detection) using Python, Fabric ML or Azure ML.
• Design end‑to‑end AI workflows, from data inputs and prompts to integration with existing systems.
• Develop and deploy rapid AI solutions with Azure OpenAI, Microsoft Copilot, ChatGPT or similar frameworks.
• Create conversational agents, copilots and retrieval‑augmented generation (RAG) tools embedded in business workflows.
• Collaborate with cross‑functional teams to define objectives, scope and deliverables.
• Present findings, provide stakeholder training and produce technical documentation to ensure reproducibility.

Required profile

• Master’s or Ph.D. in a quantitative field, or 3‑5 years of relevant data‑science experience.
• Strong proficiency in Python or R, with experience in data manipulation, visualization, machine‑learning and statistical analysis.
• Hands‑on experience with Azure cloud services and AI platforms (Azure ML, Fabric ML, Azure OpenAI).
• Familiarity with generative‑AI tools (ChatGPT, Microsoft Copilot, Power Automate, Copilot Studio) and prompt engineering.
• Knowledge of Power BI, Microsoft Fabric, REST APIs and integration methods.
• Ability to engage business users, run workshops and translate technical outcomes into business value.

Required skills

• Python
• R
• Azure ML
• Microsoft Fabric
• Azure OpenAI
• Microsoft Copilot
• ChatGPT
• Power BI
• Power Automate
• Copilot Studio
• REST APIs
• Data manipulation
• Data visualization
• Machine‑learning
• Statistical analysis
• Cloud computing (Azure)
• RAG (retrieval‑augmented generation)
• Prompt engineering