Applied AI & ML Lead [Multiple Positions Available]

JPMorgan Chase & Co.•Palo Alto, CA

1d•$189,280 - $260,000•Onsite

About The Position

JPMorgan Chase is seeking an Applied AI & ML Lead to design and develop advanced machine learning (ML) models for fraud detection and risk assessment in the financial services sector. This role involves engineering graph-based features and embeddings using Graph Neural Networks (GNNs) to identify fraudulent activity and rings within the merchant network. The lead will track model performance, ensure interpretability and compliance, and drive AI/ML innovation in trust and safety. Responsibilities include collaborating with ML serving teams for production deployment, analyzing data trends, and making strategic adjustments. The position is full-time and located in Palo Alto, CA.

Requirements

Master's degree in Computer Science, Information Technology or related field plus 2 years of experience in the job offered or as Applied Al & ML Lead, Applied Al & ML Scientist/Researcher, Software Engineer, or related occupation.
Alternatively, a Bachelor's degree in Computer Science, Information Technology or related field plus 5 years of experience in the job offered or as Applied Al & ML Lead, Applied Al & ML Scientist/Researcher, Software Engineer, or related occupation.
Developing and deploying end-to-end supervised and unsupervised ML models, including Decision Trees, XGBoost, LightGBM, and K-means, for fraud detection in the financial services or payments industry.
Working with high throughput real-time transactional data at scale.
Using Docker, Kubernetes, and CI/CD pipelines to deploy models such as gradient boosted trees or deep learning architectures.
Developing graph-based ML solutions for fraud detection with GNNs using GraphSAGE, node2vec, metapath2vec, or Graph Attention Networks.
Leveraging Pytorch Geometric or NetworkX.
Creating risk scores using temporal features, rolling aggregates, and longitudinal modeling to support fraud prevention KPIs.
Building distributed data pipelines for feature engineering and model training using PySpark, Apache Beam, Kafka, and Airflow.
Building data warehouses that focus on feature freshness and low latency using stacks that leverage BigQuery or Snowflake.
Using Python, TensorFlow, PyTorch, Scikit-learn, Keras, XGBoost, and LightGBM for fraud detection model development.
Implementing model fairness, explainability in SHAP and LIME, and compliance in a regulated financial environment.
Using model governance in the financial industry, including model risk management reviews, compliance documentation, and responding to audits.
Working with high-cardinality categorical features in embeddings and statistical smoothing for merchant-level behavior modeling or user device fingerprinting.
Conducting exploratory data analysis on large-scale, high-dimensional datasets.
Identifying signals in noisy transaction data, uncovering fraud patterns, and informing feature engineering and modeling decisions.
Extracting, transforming, and analyzing data from structured financial databases using advanced SQL techniques, including complex joins, subqueries, common table expressions, window functions, and stored procedures.
Performing scalable data processing using PySpark, BigQuery, Dask, and visualization of distributions.
Performing time series analysis using matplotlib, seaborn, and Plotly under compute and memory constraints.

Responsibilities

Design and develop advanced machine learning (ML) models to detect fraudulent merchant activity and assess payer risk.
Engineer graph-based features and embeddings by constructing transaction-level payment graphs and applying Graph Neural Networks (GNNs).
Extract and compute graph connectivity metrics (e.g., PageRank, centrality scores, community detection, label propagation) to identify fraudulent clusters and potential fraud rings.
Track and report rule-level model performance metrics, ensuring model interpretability and compliance.
Lead model development lifecycle, cross-functional initiatives, and research efforts focused on AI and ML innovation in the trust and safety domain.
Drive the adoption of scalable, explainable, and high-performing solutions for merchant fraud detection in financial services.
Analyze data trends and model outputs to identify potential areas for enhancement and to drive strategic adjustments within the division.
Collaborate with machine learning serving teams to deploy production-grade models at real-time pay-in and pay-out transaction checkpoints.
Ensure low-latency fraud detection and integration with business-critical systems.