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)

Education

Bachelor of Engineering