Data Scientist - Senior Manager- Consulting

Location:

Anywhere in Country

EY delivers unparalleled cross‑functional tech consulting services in artificial intelligence, big data and cloud engineering. Our Artificial Intelligence and Data team helps apply cutting‑edge technology and techniques to bring solutions to our clients. As part of that, you will sit side‑by‑side with clients and diverse teams from EY to create a well‑rounded approach to advising and solving challenging problems, some of which have not been solved before.

Come help define the future of AI.

We are looking for a Senior Manager, Data Scientist to be a senior technical leader across our most important AI bets driving applied research, leading multi‑disciplinary teams, and shaping how EY designs, builds, and scales AI solutions for our clients.

Your work will span agentic AI, foundation‑model applications, retrieval and grounding, knowledge representation, machine learning, optimisation, and the broader analytics landscape. A current strategic focus is the cognitive harness and memory layer that powers our agentic offerings and you will play a leading role there but the role is intentionally broader. You will move across problem spaces as our practice and our clients’ priorities evolve, and you will help define which problems we tackle next.

This is a high‑visibility, high‑impact role at the intersection of applied research, engineering, and client delivery. You will be hands‑on, you will publish, you will prototype and productise, and you will mentor other data scientists and ML engineers tackling the hardest problems in EY’s AI portfolio.

• Lead data‑science strategy and execution across multiple AI work streams including agentic AI, the cognitive harness, memory and grounding, foundation‑model applications, machine learning, optimisation, and applied analytics.
• Drive the applied‑research and experimentation agenda long‑context vs. retrieval trade‑offs, hybrid search, reranking, evaluation design, model selection, fine‑tuning, and emerging techniques as the field evolves.
• Design and contribute to the build of core platform components including elements of EY’s cognitive harness (memory services, retrieval pipelines, grounding layers, evaluation harnesses) and other reusable AI capabilities.
• Establish evaluation and quality frameworks for AI systems retrieval and grounding fidelity, hallucination rate, model accuracy, latency, cost, fairness, and continuous evaluation in production.
• Lead and mentor data scientists and ML engineers set the technical bar, run design reviews, and grow rigor in experimentation, model selection, and production readiness.
• Partner with sector and functional teams (Finance, Risk, Tax, Supply Chain, HR, Operations) to translate business problems into rigorous, deliverable data‑science programmes.
• Engage with clients on complex AI problems, shape the technical approach, defend it in front of senior stakeholders, and own delivery quality.
• Stay abreast of AI technology trends and provide directional guidance and recommendations around models, frameworks, tools, and patterns that fit our clients’ existing ecosystems.
• Apply combined business and technical knowledge to develop and execute target architectures that enable implementation, monitoring, and ongoing improvement of AI at scale.

• Strong, hands‑on data scientist who codes comfortable building and shipping models, pipelines, services, and experimental frameworks, not just specifying them.
• Deep technical breadth across modern AI foundation models and prompting, retrieval and grounding, embeddings and fine‑tuning, classical ML, optimisation, and experimentation rigor.
• Working knowledge of agentic systems and cognitive harness / agent‑runtime architectures – memory, tools, policies, evaluation, observability, cost and the trade‑offs between them.
• Familiarity with at least one major agent SDK (Google ADK, AWS Bedrock Agent Core, Lang Graph, Auto Gen, OpenAI Agents SDK) and the ability to compare them critically.
• Experience designing and operating production AI systems on cloud platforms (GCP, AWS,…