Fraud AI/ML Platform Product Director

JPMorgan Chase & Co.•New York, NY

10h•$180,500 - $285,000

About The Position

As a Product Director in the Consumer & Community Banking Fraud Strategy organization, you will play a pivotal role in defining the future of fraud prevention through advanced machine learning and artificial intelligence. In this senior leadership position, you will own the product vision and multi-year roadmap for a next-generation, real-time fraud intelligence platform that protects millions of customers across digital banking, payments, and financial ecosystems. Leverage your expertise in transformer-based ML, graph intelligence, continuous learning, and modern MLOps, you will build capabilities to detect fraud rings, coordinated attacks, and multi-step fraud schemes. You will drive innovation in dynamic feature engineering, graph-based risk detection, sequence modeling, and experimentation frameworks to rapidly deliver governed fraud models that protect customers. Partnering with Fraud Operations, Data Science, Engineering, Product, and Model Risk Management, you will deliver measurable fraud-loss reduction while preserving an excellent customer experience. You will also communicate platform strategy, experiment outcomes, and capability roadmaps to senior leaders to inform decisions and continuously improve fraud prevention.

Requirements

8+ years of experience or equivalent expertise delivering products, projects, or technology applications
Extensive knowledge of the product development life cycle, technical design, and data analytics
Proven ability to influence the adoption of key product life cycle activities including discovery, ideation, strategic development, requirements definition, and value management
Experience driving change within organizations and managing stakeholders across multiple functions
Bachelor's degree
5+ years building or owning ML-enabled products such as feature platforms, model platforms, or fraud decisioning systems in production environments.
Deep expertise in fraud, payments risk, trust and safety, cybersecurity, or similarly adversarial domains where models face adaptive threats.
Strong technical fluency across applied machine learning, data systems, and production constraints including latency, reliability, monitoring, and scale.
Proven track record of leading cross-functional execution with Product, Engineering, Data Science, Operations, and Model Risk Management teams.
Exceptional communication and executive presence; comfortable translating technical capabilities into business value for senior leadership.
Strong analytical skills and ability to define success metrics, evaluate experimentation results, and make data-driven platform decisions.

Nice To Haves

Recognized thought leader within a related field
Advanced degree in Computer Science, Machine Learning, Statistics, or related quantitative field
Hands-on experience building and scaling transformer-based or other large-scale sequence models in production, using high-volume event data (e.g., fraud, security, behavioral analytics, risk).
Experience productizing graph features/embeddings or graph ML for fraud ring detection, network analysis, or risk assessment.
Proven RL system design in live environments (optimization/control/fraud decisioning), including reward design, online/offline evaluation, and safe deployment in adversarial settings.
Experience designing feature stores and maintaining online/offline parity at scale for real-time decisioning systems.
Strong representation learning/embeddings and long-horizon temporal modeling skills, familiarity with financial services model governance, and demonstrated thought leadership (papers, patents, talks, or open source).

Responsibilities

Oversees the product roadmap, vision, development, execution, risk management, and business growth targets
Leads the entire product life cycle through planning, execution, and future development by continuously adapting, developing new products and methodologies, managing risks, and achieving business targets like cost, features, reusability, and reliability to support growth
Coaches and mentors the product team on best practices, such as solution generation, market research, storyboarding, mind-mapping, prototyping methods, product adoption strategies, and product delivery, enabling them to effectively deliver on objectives
Owns product performance and is accountable for investing in enhancements to achieve business objectives
Monitors market trends, conducts competitive analysis, and identifies opportunities for product differentiation
Own the multi-year platform strategy and roadmap for fraud models, dynamic feature infrastructure (incl. streaming + feature store), graph intelligence, and MLOps across CCB payment and banking products.
Lead experimentation and delivery with clear success criteria/lift metrics, converting validated POCs into production capabilities that reduce fraud loss and improve customer experience.
Productize graph intelligence for fraud rings (entity schema, graph features/embeddings, freshness/latency SLAs, and explainability requirements).
Establish end-to-end model lifecycle standards (model CI/CD, evaluation gates, monitoring, drift detection, automated retraining, and rollback) to ensure safe, reliable deployment.
Embed governance by design including explainability, bias/fairness checks, and Model Risk documentation to meet regulatory expectations.
Build strong partnerships and team capability by developing a high-performing product org, collaborating cross-functionally (Product, Engineering, Data Science, Fraud Ops, MRM), staying ahead of industry trends, and translating technical topics for executives.