Data Scientist - Senior Manager- Consulting - Location OPEN

EY Société d'Avocats•Akron, NY

1d•Hybrid

About The Position

EY delivers unparalleled cross-functional tech consulting services in artificial intelligence, big data, and cloud engineering. We support and enable big ideas with the ambition to keep doing more, leveraging leading practices and extensive experience to ensure the highest level of execution and client satisfaction. Our Artificial Intelligence and Data team applies cutting-edge technology and techniques to provide solutions for our clients. You will work alongside clients and diverse EY teams to create a well-rounded approach to advising and solving challenging problems, some of which have not been solved before. EY is significantly investing in our award-winning AI & Data practice and is looking for a Senior Manager, Data Scientist to be a senior technical leader across our most important AI bets. This role will drive applied research, lead multi-disciplinary teams, and shape 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, optimization, 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, publish, prototype and productize, and mentor other data scientists and ML engineers tackling the hardest problems in EY's AI portfolio.

Requirements

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, optimization, 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 AgentCore, LangGraph, AutoGen, OpenAI Agents SDK) and the ability to compare them critically.
Experience designing and operating production AI systems on cloud platforms (GCP, AWS, Azure, Databricks) including responsible-AI guardrails, monitoring, and continuous evaluation.
Track record of leading data-science teams across multiple projects setting technical strategy, mentoring, and raising the bar on rigor and delivery quality.
Excellent communication skills able to explain complex AI concepts to executive clients, and able to defend technical choices in front of architects, engineers, and researchers.
Comfortable moving between problem spaces — equally credible designing a memory architecture, evaluating a forecasting model, or shaping a new agentic workflow.
PhD preferred in Computer Science, Machine Learning, Computational Linguistics, Statistics, Applied Mathematics, Operations Research, or a closely related quantitative field. Master's with exceptional applied experience also considered.
12+ years of applied data science / ML experience, with at least 3 years in a senior technical leadership role.
Demonstrable experience taking AI and ML systems from prototype to production — across multiple problem types (e.g., predictive modeling, NLP, retrieval / RAG, optimization, or agentic workflows).
Hands-on proficiency in Python and the modern ML/AI stack (PyTorch, Hugging Face, scikit-learn, and at least one of LangChain / LangGraph or an equivalent agent / LLM framework).
Practical experience with at least one major cloud AI platform (Vertex AI, Bedrock, Azure AI Foundry, Databricks Mosaic AI) and one vector / retrieval stack.
Experience designing evaluation frameworks for AI systems — beyond standard benchmarks — including evaluation for foundation-model-based or agentic workflows.
Experience operating in a client-facing or cross-functional environment, translating business problems into rigorous data-science programs.

Nice To Haves

Published research or open-source contributions in AI / ML agentic systems, retrieval, memory and grounding, knowledge graphs, foundation models, optimization, or related areas.
Experience with knowledge graphs, ontologies, and neuro-symbolic approaches to grounding and reasoning.
Experience building or contributing to a cognitive harness, agent operating system, or agent runtime — internal or open source.
Experience with continual learning, online evaluation, drift detection, and model / memory monitoring in production.
Familiarity with regulated-industry constraints (SOX, HIPAA, GDPR) and how AI design interacts with audit, retention, and explainability.
Prior consulting, product, or hyperscaler experience — comfortable in a fast, ambiguous environment with senior stakeholders.

Responsibilities

Lead data-science strategy and execution across multiple AI workstreams including agentic AI, the cognitive harness, memory and grounding, foundation-model applications, machine learning, optimization, 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 programs.
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.