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.

Experience
5-9 yrs