Senior Data Scientist

Responsibilities:

• Own data science problems end-to-end, from ambiguous business or clinical question to scoped problem, model, and measurable outcome.
• Develop predictive and statistical models across health domains: supply chain demand forecasting, claims and denial analytics, and population health indicators.
• Design experiments and validation approaches defining success metrics, baselines, and evaluation criteria before building.
• Work with large-scale, multi-country datasets, handling the messiness, gaps, and inconsistencies of real-world health data.
• Build and improve natural-language-to-data capabilities in LLM- and RAG-based systems that let users query health databases in plain language, including text-to-SQL, retrieval over schema and documentation, and grounding model outputs in trusted data.
• Apply NLP and generative AI techniques to unstructured health data clinical notes, documents, and free-text fields where structured extraction adds value.
• Partner with data engineers to productionise models, feature pipelines, retraining, monitoring, and drift detection rather than handing off notebooks.
• Collaborate with product and domain teams to translate clinical and operational questions into well-scoped data problems with clear success criteria.
• Communicate findings and their limitations honestly to technical and non-technical stakeholders, including government counterparts.
• Set analytical standards for the team: methodology rigour, reproducibility, and documentation and mentor junior analysts and data scientists.
• Contribute to architecture and tooling decisions for the analytics and ML stack.

Requirements:

• 5+ years of experience in data science, with a track record of shipping models and analyses that reached real users or decisions.
• Demonstrated experience on large-scale projects working with high-volume data and systems where scale and performance matter.
• Proven ability to handle ambiguity: you can take an open-ended problem, structure it, and drive it to a defensible answer with limited direction.
• Strong Python for data science: Pandas, NumPy, scikit-learn, and at least one deep learning or advanced modelling framework where relevant.
• Strong SQL and hands-on experience with large or analytical datasets; comfort with columnar databases such as ClickHouse, BigQuery, Snowflake, or DuckDB is a plus.
• Solid grounding in statistics and machine learning fundamentals: you can choose the right method and explain why, and you know the difference between correlation and a usable signal.
• Hands-on experience with Natural Language Processing (NLP), text processing, embeddings, and language model fundamentals.
• Practical experience with Generative AI and Large Language Models (LLMs) prompting, evaluation, and building applications on top of foundation models via APIs or open-weight models.
• Experience designing and building Retrieval-Augmented Generation (RAG) systems chunking, embeddings, vector search, and retrieval quality and an understanding of how to ground LLM outputs to reduce hallucination.
• Familiarity with natural-language-to-SQL / text-to-data approaches and their failure modes, especially the accuracy and safety concerns of letting an LLM query a real database.
• Experience taking models to production and maintaining them, monitoring, retraining, and model quality over time, not just training and handing off.
• Clear communication: you can defend a methodology to a technical peer and explain the same result plainly to a non-technical decision-maker.
• Strong data quality and epistemic discipline: you surface uncertainty and caveats rather than overstating confidence.