Applied AI/ML Lead - Vice President - Payments

JPMorgan Chase & Co.•New York, NY

About The Position

As part of the Commercial & Investment Bank, JPMorgan Chase Payments enables organizations of all sizes to execute transactions efficiently and securely, transforming the movement of information, money, and assets. We tackle complex challenges across the payments lifecycle with solutions that facilitate seamless transactions across borders, industries, and platforms. Operating in over 160 countries and handling more than 120 currencies, we are a leading processor of USD payments with daily transaction volumes in the trillions. As a Vice President, Applied AI/ML Lead (Sr Level IC role) within JPMorgan Chase Payments, you will own and drive the end-to-end delivery of high-impact AI/ML capabilities across the payments ecosystem—while remaining deeply hands-on. You will apply modern neural networks (including Transformers) to build, fine-tune, distill, and deploy models that improve fraud/risk outcomes, operational automation, and client experience, operating under real-world constraints (latency, scale, reliability, explainability, and governance). You will set technical direction, establish standards, and lead execution through influence—partnering closely with Product, Engineering, Risk, Compliance, and Data teams.

Requirements

Master’s or PhD in Computer Science, Machine Learning, Statistics, Mathematics, Operations Research, or related field, plus 6+ years of industry experience delivering applied ML (or PhD with equivalent applied experience).
Demonstrated track record of shipping ML models into production with measurable business impact, including iteration post-launch (monitoring, retraining, recalibration, and continuous improvement).
Strong hands-on expertise in neural networks and Transformers, including practical experience with: Fine-tuning strategies (e.g., full fine-tuning and parameter-efficient methods), Distillation / compression (teacher–student, quantization-aware approaches, latency/cost-driven optimization), Robust evaluation and failure-mode analysis for real-world deployment
Strong software engineering skills in Python, with deep experience in PyTorch or TensorFlow and standard ML/data libraries.
Experience building data-driven systems using SQL and distributed processing (e.g., Spark/PySpark or equivalent).
Cloud and production experience on AWS (or equivalent cloud), including deploying services/pipelines and operating them reliably at scale.
Ability to take ambiguous business problems and turn them into structured ML plans: data strategy, modeling approach, evaluation, rollout, and operationalization.
Excellent communication skills, including explaining tradeoffs (accuracy vs latency, risk vs customer friction, complexity vs maintainability) to both technical and non-technical audiences.

Nice To Haves

Payments domain experience: fraud/risk, transaction monitoring, identity/account takeover, disputes/chargebacks, sanctions/AML-adjacent signal work, payment exceptions and investigations, routing/authorization optimization, or treasury/transaction banking.
Experience with streaming/real-time architectures and feature generation (e.g., event-driven systems, point-in-time correctness, leakage prevention).
Strong ML platform/MLOps exposure: feature stores, model registries, CI/CD for ML, scalable training/inference, observability, and governance workflows.
Experience with Docker/Kubernetes and modern data platforms (e.g., Databricks, Snowflake) where relevant.

Responsibilities

Own a major AI/ML problem area end-to-end (e.g., transaction risk & fraud, anomaly detection, payment exceptions automation, routing/authorization optimization), from opportunity sizing and problem framing through production rollout and iteration.
Design and build production-grade ML systems that operate at payments scale, balancing accuracy, latency, throughput, and cost across batch, near-real-time, and real-time inference patterns.
Develop state-of-the-art neural approaches including Transformer architectures, representation learning, and sequence/graph methods where appropriate; apply fine-tuning (full/parameter-efficient), distillation, and model compression techniques to meet deployment constraints.
Define rigorous evaluation and measurement: offline metrics, calibration, robustness testing, segmentation, and online experimentation where feasible; translate model lift into business impact (loss reduction, approval rates, false-positive reduction, ops productivity, client outcomes).
Establish model lifecycle standards: reproducibility, testing, monitoring/alerting, drift detection, champion–challenger approaches, incident response/rollback, and ongoing performance governance.
Partner with Risk/Compliance and model governance stakeholders to ensure appropriate documentation, controls, explainability/interpretability as required, and audit-ready processes.
Lead through influence: drive technical decisions via design reviews, code/model reviews, and mentoring; raise the bar across applied scientists and ML engineers through best practices and pragmatic standards.
Communicate clearly to senior stakeholders, including tradeoffs, limitations, and recommended actions; make complex model behavior and risk/benefit understandable and decision-ready.