Senior Data Scientist

Overview

We are developing a high-precision Medical Decision-Support Engine. Our system leverages the reasoning capabilities of LLMs grounded in structured Medical Knowledge Graphs to provide clinicians with evidence-based decision support. We are looking for a Data Scientist who can bridge the gap between "black-box" AI and safe, interpretable clinical practice.

Responsibilities

• Dataset Engineering & Validation: Design robust pipelines for processing Electronic Health Records (EHR) and medical literature. Implement rigorous multi-stage validation frameworks (Sensitivity/Specificity analysis) to ensure clinical safety and model reliability.
• LLM Fine-tuning: Adapt Large Language Models using SFT, DPO, or PEFT (LoRA/QLoRA) for specialised medical domains and complex clinical diagnostic reasoning.
• Advanced RAG & Graph RAG: Architect hybrid retrieval systems that combine vector databases with Knowledge Graphs to eliminate hallucinations and ensure factual grounding.
• Explainability & Interpretability: Develop methods to make model outputs transparent. The engine must provide "reasoning paths" — justifying recommendations by citing specific medical evidence, clinical protocols, and graph relations.

Technical Requirements

• GenAI Stack: Expert knowledge of Transformer architectures and hands-on experience fine-tuning LLMs (Llama 3, Mistral, etc.) 
• Graph ML: Hands-on experience with Knowledge Graphs, Triple-stores, or Graph Databases (Neo4j, ArangoDB) and Graph Neural Networks (GNNs).
• Retrieval Systems: Proficiency in LangChain / LlamaIndex and vector search engines (Pinecone, Milvus, or Weaviate).
• XAI Tools: Practical experience with SHAP, LIME, or custom attention-mapping techniques for model interpretability.
• Validation & Stats: Strong background in statistical validation for high-stakes environments and handling imbalanced medical data.

Why join our team?

• Solve the "Why": You won't just build a model; you'll build a system that clinicians can trust because they understand its logic.
• Cutting-Edge Stack: Work at the absolute forefront of AI, combining LLMs with structured Knowledge Graphs (Graph RAG).
• Meaningful Social Impact: Your work directly contributes to better patient outcomes, faster recovery, and a significant reduction in diagnostic errors.

If you are passionate about pushing the boundaries of what AI can do in healthcare, we would love to hear from you.