As aVice President, Applied AI/ML Lead (Sr Level IC role)within JPMorgan Chase Payments, you willown and drivethe end-to-end delivery of high-impact AI/ML capabilities across the payments ecosystem nd remain deeply hands-on. You will applymodern neural networks (including Transformers)to build, fine-tune, distill, and deploy models that improvefraud/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 nd partner closely with Product, Engineering, Risk, Compliance, and Data teams.

Job 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 systemsthat 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 approachesincludingTransformer architectures, representation learning, and sequence/graph methods where appropriate; applyfine-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 6challenger approaches, incident response/rollback, and ongoing performance governance.
• Partner with Risk/Compliance and model governance stakeholdersto 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.

Required qualifications, capabilities, and skills

• Masters or PhDin Computer Science, Machine Learning, Statistics, Mathematics, Operations Research, or related field, plus6+ yearsof industry experience delivering applied ML (or PhD with equivalent applied experience).
• Demonstrated track record ofshipping ML models into productionwith measurable business impact, including iteration post-launch (monitoring, retraining, recalibration, and continuous improvement).
• Strong hands-on expertise inneural networks and Transformers, including practical experience with:
  • Fine-tuningstrategies (e.g., full fine-tuning and parameter-efficient methods)
  • Distillation / compression(teacher 6student, quantization-aware approaches, latency/cost-driven optimization)
  • Robustevaluationand failure-mode analysis for real-world deployment
• Strong software engineering skills inPython, with deep experience inPyTorch or TensorFlowand standard ML/data libraries.
• Experience building data-driven systems usingSQLand distributed processing (e.g.,Spark/PySparkor equivalent).
• Cloud and production experience onAWS(or equivalent cloud), including deploying services/pipelines and operating them reliably at scale.
• Ability to take ambiguous business problems and turn them intostructured 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.

Preferred qualifications, capabilities, and skills

• 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 withstreaming/real-timearchitectures 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 withDocker/Kubernetesand modern data platforms (e.g.,Databricks,Snowflake) where relevant.