Lead Data Scientist

KEY RESPONSIBILITIES: 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. EXPERIENCE AND QUALIFICATIONS: - 8+ years hands-on in AI/ML and Data Science, with models that reached production and changed a business metric; a record of leading a team - hiring, setting standards and developing next-in-line leaders. - Strong command of Python and SQL across large datasets, with robust, testable code and sound software architecture. - Depth in feature engineering, statistics and ML algorithms (regression, classification, clustering, neural networks, time-series), and in Generative AI, NLP and Computer Vision and their business applications. - Hands-on with language models for retrieval - embeddings, RAG, vector stores, prompt design and evaluation - plus MLOps and engineering discipline to ship: experiment tracking, model registry, versioning, containerisation, CI/CD and cloud AI services (AWS / GCP / Azure). - The executive presence to hold a commercial argument, not only a technical one; genuine comfort with ambiguity; and awareness of data privacy, governance and responsible-AI obligations. - A BS or MS in Computer Science, AI/ML, Data Science or a related field. - We index on what you have shipped, not on further qualifications.