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.

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: • 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)