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.