Applied AIML -Executive Director

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 anAI/ML Executive Director withinConsumer & Community Banking (CCB) Recommendations & Personalization , you will lead the design and delivery of large-scalerecommendation, ranking, and personalization systems that power customer experiences across CCB digital channels. You’ll drive strategy and execution acrosssearch/retrieval and NLP signals , experimentation, measurement, and production ML—leveraging GenAI where it meaningfully improves relevance, efficiency, or customer outcomes (but withrecommenders and personalization as the core focus ).

Job 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 frameworksfor ranking and personalization (e.g., relevance/utility metrics, calibration, novelty/diversity, long-term value, bias/fairness considerations, and guardrails).
• Apply NLP and search/retrieval techniquesto enrich signals (query/document understanding, embeddings, semantic retrieval, entity/intent extraction) that improve recommendation quality and explainability.
• Use GenAI pragmaticallyto 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 teamof applied scientists and ML engineers; set technical direction, raise engineering quality, and provide coaching and career development.
• Partner cross-functionallywith 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 learningby introducing modern recommendation methods, experimentation best practices, and strong documentation and knowledge sharing.

Required qualifications, capabilities, and skills:

• PhD in Computer Science (or equivalent experience)with strong research and industry background inmachine learning, with depth inrecommender systems, ranking, personalization, or information retrieval.
• Proven ability to lead and deliverlarge-scale production ML systemsusing big data, including recommenders (collaborative filtering, deep retrieval/ranking, sequence models), classification/regression, and causal/experimental methods.
• Strong track record ofpeople 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+ yearsof hands‑on programming and system-building experience (PhD + industry); strong inPythonand at least one ofScala/Java; experience withSparkand distributed data processing.
• Solid fundamentals indata structures, algorithms, distributed systems, and databases, and experience building scalable, reliable ML services.

Preferred qualifications, capabilities, and skills:

• Deep expertise inranking/retrieval/search(semantic retrieval, ANN/vector search, learning-to-rank),online experimentation, andreal-time personalization.
• Experience designingfeature stores, streaming/real-time pipelines, low-latency inference, and ML observability (data/model drift, performance diagnostics).
• Experience applyingNLP/LLMsto improve recommendation systems (embeddings, query understanding, content signals, retrieval augmentation) with disciplined evaluation and governance.
• Familiarity withresponsible AIconsiderations relevant to personalization (fairness, explainability, privacy, and control frameworks).