Applied AIML -Executive Director

JPMorgan Chase & Co.•New York, NY

About The Position

Our Consumer & Community Banking division serves our Chase customers through a range of financial services, including personal banking, credit cards, mortgages, auto financing, investment advice, small business loans and payment processing. We’re proud to lead the U.S. in credit card sales and deposit growth and have the most-used digital solutions – all while ranking first in customer satisfaction. In this role, you’ll apply strong technical judgment to choose the right approaches (including modern LLM-based methods where appropriate), evaluate performance with rigorous metrics, and ensure solutions are reliable, secure, and scalable in real-world environments. You’ll also contribute to improving data quality and feedback loops, monitoring models in production, and continuously iterating to reduce agent effort, shorten resolution times, and increase consistency and quality across operational workflows. As an AI/ML Executive Director within Consumer & Community Banking (CCB) Recommendations & Personalization, you will lead the design and delivery of large-scale recommendation, ranking, and personalization systems that power customer experiences across CCB digital channels. You’ll drive strategy and execution across search/retrieval and NLP signals, experimentation, measurement, and production ML—leveraging GenAI where it meaningfully improves relevance, efficiency, or customer outcomes (but with recommenders and personalization as the core focus).

Requirements

PhD in Computer Science (or equivalent experience) with strong research and industry background in machine learning, with depth in recommender systems, ranking, personalization, or information retrieval.
Proven ability to lead and deliver large-scale production ML systems using big data, including recommenders (collaborative filtering, deep retrieval/ranking, sequence models), classification/regression, and causal/experimental methods.
Strong track record of people leadership (building teams, setting technical direction, mentorship, performance management).
Excellent written and verbal communication skills, including influencing senior stakeholders and translating business goals into measurable ML outcomes.
10+ years of hands-on programming and system-building experience (PhD + industry); strong in Python and at least one of Scala/Java; experience with Spark and distributed data processing.
Solid fundamentals in data structures, algorithms, distributed systems, and databases, and experience building scalable, reliable ML services.

Nice To Haves

Deep expertise in ranking/retrieval/search (semantic retrieval, ANN/vector search, learning-to-rank), online experimentation, and real-time personalization.
Experience designing feature stores, streaming/real-time pipelines, low-latency inference, and ML observability (data/model drift, performance diagnostics).
Experience applying NLP/LLMs to improve recommendation systems (embeddings, query understanding, content signals, retrieval augmentation) with disciplined evaluation and governance.
Familiarity with responsible AI considerations relevant to personalization (fairness, explainability, privacy, and control frameworks).

Responsibilities

Own and evolve CCB recommendation & personalization platforms (candidate generation, ranking, re-ranking, retrieval, and real-time decisioning) to improve customer relevance and engagement across journeys and surfaces.
Lead end-to-end ML delivery: problem framing, feature strategy, model development, offline/online evaluation, A/B testing, launch, monitoring, and iteration for production recommender systems.
Develop and operationalize evaluation frameworks for ranking and personalization (e.g., relevance/utility metrics, calibration, novelty/diversity, long-term value, bias/fairness considerations, and guardrails).
Apply NLP and search/retrieval techniques to enrich signals (query/document understanding, embeddings, semantic retrieval, entity/intent extraction) that improve recommendation quality and explainability.
Use GenAI pragmatically to augment the recommendation stack (e.g., content understanding, synthetic labeling, summarization, conversational retrieval, or post-processing) with strong controls, evaluation, and risk awareness.
Build and lead a high-performing team of applied scientists and ML engineers; set technical direction, raise engineering quality, and provide coaching and career development.
Partner cross-functionally with Product, Design, Data, Risk/Controls, and Engineering to align on goals, prioritize roadmaps, and deliver measurable customer and business impact.
Be a hands-on technical leader: contribute to architecture and critical code paths; guide system design for low-latency services, feature pipelines, training/inference infrastructure, and reliability.
Promote a culture of rigor and learning by introducing modern recommendation methods, experimentation best practices, and strong documentation and knowledge sharing.