Job Description
-Knowledge of work with US healthcare data (claims, clinical records, authorization, provider contracts)
-Proficiency in deep learning frameworks PyTorch, Tensorflow or other
-Experience with Agentic AI frameworks such as LangChain, LlamaIndex, and other orchestration tools for autonomous agents.
-Hands-on experience implementing/adapting published academic research into tangible solutions.
-Ability to understanding emerging prompting strategies/research and evaluate benefits/trade-offs.
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Skills and Requirements
-Advanced degree in Applied Mathematics, Physics, Computer Science, Statistics, or a related technical field.
Proficiency in traditional machine learning and statistical modelling, including regression, classification, sampling design.
-Strong coding skills in Python, SQL, and related languages.
Demonstrated systems-level thinking and a track record of creative problem-solving, including non-standard approaches to complex challenges.
-Proven experience in transitioning projects from proof-of-concept (PoC) to production, with a focus on scalability, reliability, and performance.
-Exposure to LLM technologies The team is made up of 6 AI engineers - lots of good experience in Agentic AI/LLMs and the person will be well supported by a mature team for the duration.
The client is seeking a Senior Data Scientist for a sharp, high-impact focused on delivering practical AI/ML solutions that improve efficiency, prioritization, and medical cost savings within Payment Integrity operations.
This is an ideal opportunity for a contractor who enjoys stepping into a well-defined piece of work, moving quickly, and partnering with a mature, collaborative team. You'll work on meaningful problems with modern tooling, including LLMs, RAG, GenAI, predictive modelling, and agentic workflows, in an environment that values autonomy, technical depth, and low-friction delivery.
What you'll be doing in this engagement:
-Design and deploy intelligent models by applying advanced statistical techniques, traditional machine learning algorithms, deep learning architectures, and modern approaches such as Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Agentic AI.
-Leverage state-of-the-art libraries and frameworks to ensure scalable, production-grade solutions aligned with enterprise deployment standards.
-Lead cross-functional discovery sessions with Data Scientists and Payment Integrity Operations leadership to translate business challenges into actionable analytical objectives. Drive alignment between statistical rigor and emerging AI capabilities to maximize operational impact.
-Deliver robust statistical analyses that frame business scenarios with clarity and precision, integrating classical inference methods with generative and agentic reasoning to inform critical decisions and enhance process outcomes.
-Collaborate with data stewards and engineering teams to curate high-quality datasets for model development. Ensure data integrity and readiness for both traditional predictive modelling and autonomous agent workflows.
-Develop end-to-end modelling pipelines encompassing sampling design, data and selection of appropriate statistical and AI methodologies. Document processes and results with transparency, supporting reproducibility and auditability across both ML and LLM-based systems.
Communicate insights and model outputs effectively to stakeholders, translating complex statistical and AI-driven findings into actionable business narratives.
Monitor and refine model performance through continuous evaluation, incorporating feedback loops and adaptive learning mechanisms. Recommend enhancements to algorithms and agentic workflows that unlock new insights and drive measurable improvements.
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