Data Scientist

Delivers machine learning and modern AI (including generative AI where relevant) solutions end-to-end, from problem framing to production deployment, partnering closely with product, data engineering and business teams.

Responsibilities

• Translate business needs into ML/AI problem statements and measurable success metrics.
• Develop ML models and, where relevant, GenAI components (e.g., retrieval-augmented generation, prompt pipelines) with clear evaluation criteria.
• Run evaluation: offline metrics, error analysis, bias checks, and monitoring baselines; document decisions and assumptions.
• Communicate results and limitations clearly to technical and non-technical stakeholders; support adoption in workflows.

Technical requirements

• Experience: 4-6+ years delivering end-to-end data science projects.
• Education: Master’s or PhD in Data Science, Machine Learning, Statistics, Computer Science, Applied Mathematics, or related quantitative field (required from this level onward).
• Core stack: Python, Spark, Git; ML frameworks; Databricks/MLflow (or equivalent); cloud basics.

Additional responsibilities

• Python (pandas, numpy) + Git for reproducible development
• Databricks (Notebooks, Workflows) for development and orchestration
• ML flow (experiments, tracking, model registry) for lifecycle management
• Azure (cloud services; where relevant Azure OpenAI and Azure AI Foundry for GenAI build/evaluation)
• Databricks Mosaic AI (including Mosaic AI Model Serving) for GenAI delivery in the lakehouse
• Databricks Vector Search for RAG retrieval patterns
• Unity Catalog for governed data and model access (where applicable)
• Lakehouse Monitoring / model monitoring for quality and drift (where applicable)

Preferred skills

Technology->AI-Data science->Data Science - Basic,Technology->AI-Generative AI->Generative AI - Basic,Technology->Cloud Platform->Azure Analytics Services->Azure Databricks

Education

Bachelor of Engineering