Data Scientist

Models and Machine Learning

• Conceptualise business problems, drive frameworks, and translate ambiguous asks into solvable analytical problems.
• Transform data science prototypes into production-grade solutions; design AI/ML applications against defined business and technical requirements.
• Leverage large language and vision-language models for retrieval, extraction and reasoning over unstructured enterprise data.
• Find and implement the right algorithms and tools, balancing accuracy, latency, interpretability and cost; train, evaluate and refine.
• Integrate models into the application flow and deploy at scale, with monitoring for drift, degradation and failure.

Data Foundation and Infrastructure

• Set up the infrastructure for data analysis and mining required to generate actionable insight reliably.
• Use effective feature engineering and pre-processing across structured and unstructured data; select or define annotated datasets and their quality controls.
• Extend ML libraries and frameworks so they apply across a range of tasks.

Responsible AI, Measurement and Governance

• Establish responsible-AI practice — model documentation, bias and privacy review, PII minimisation and audit trails.
• Institutionalise measurement — A/B tests, holdouts and causal inference — so every model carries a defensible business number.
• Create dashboards and visualisations that present data in a logical, decision-ready way to stakeholders.

Team and Stakeholders

• Set up and lead your own team, drive the vertical, and develop next-in-line leaders.
• Collaborate with cross-functional teams of diverse backgrounds; communicate insight coherently, working directly with the senior-most leadership