• You will lead development of new fraud prediction models using a mix of approaches for tabular, graph, and behavioral data • You will build and scale feature pipelines and training datasets from proprietary and third-party signals, partnering with data and platform teams when needed. • You will prototype new modeling ideas and features, run offline experiments, and drive the best-performing approaches into production with appropriate risk controls. • You productionize models: integrate into batch and/or real-time decision systems, and improve reliability, latency, and operational robustness. • You will instrument and monitor model and data health, and help define retraining/backtesting workflows as fraud patterns evolve. • Identify and implement foundational improvements to how the team builds models. • You will collaborate across Engineering, Fraud Analytics, Product, and ML Platform to define requirements, evaluate tradeoffs, and communicate results clearly to both technical and non-technical audiences.

• Lead the design and development of machine learning systems and LLM-based applications • Fine-tune and adapt large language models for domain-specific use cases • Build NLP pipelines to extract structured data from complex, unstructured documents • Translate ambiguous product goals into clear technical and ML execution plans • Work directly with product signals, user feedback, and backlog input to prioritize development work • Build evaluation systems to measure model performance and detect regressions • Design automated benchmarking pipelines for continuous model testing • Define meaningful performance metrics tied to real-world outcomes • Build feedback loops that use user corrections to continuously improve model performance • Apply computer vision techniques to extract data from PDFs and scanned documents • Develop AI agents that interact with web systems using automation tools such as Playwright or Selenium • Work across system design, infrastructure, training, and inference pipelines • Build scalable ML systems and supporting infrastructure from the ground up

• Design, build, and maintain ML Ops pipelines supporting model training, validation, and deployment across AWS environments. • Implement automation for model packaging, testing, deployment, and monitoring using CI/CD best practices. • Collaborate with data engineers and data scientists to operationalize ML workloads within the data lakehouse ecosystem. • Develop and maintain integrations between data ingestion, feature stores, and model repositories. • Apply infrastructure-as-code (Terraform, AWS CDK, CloudFormation) to automate ML pipeline infrastructure. • Implement and manage model versioning, reproducibility, and lineage tracking using tools such as MLflow or SageMaker Model Registry. • Define and automate monitoring, alerting, and retraining strategies for deployed models. • Ensure all ML infrastructure and pipelines meet enterprise security, compliance, and governance standards. • Participate in code reviews, knowledge sharing, and continuous improvement of ML Ops practices. • Mentor junior engineers and contribute to documentation, standards, and best practices for ML Ops across teams.

• Provide strategic and technical leadership to the ML team, ensuring alignment with best practices and industry standards. • Recruit, hire, and onboard top talent, including data scientists and machine learning engineers, to strengthen the team. • Offer mentorship and guidance, helping team members develop their technical skills and advance their careers. • Foster a positive, inclusive, and innovative team culture that promotes collaboration and knowledge-sharing. • Conduct regular performance reviews, set clear objectives, and provide constructive feedback to drive continuous improvement. • Oversee and manage complex ML projects from initial concept to successful deployment. • Ensure the delivery of high-quality outputs on time and within budget, maintaining rigorous standards throughout the project lifecycle. • Collaborate with Delivery Managers to define project scope, timelines, resource allocation, and key milestones. • Proactively track project progress, identify potential risks, and implement effective mitigation strategies to keep projects on track. • Build and maintain strong, trusted relationships with clients, gaining a deep understanding of their business objectives and challenges. • Communicate complex technical concepts clearly and effectively to both technical and non-technical client stakeholders. • Present project findings, insights, and actionable recommendations in a compelling and client-friendly manner, ensuring alignment with their goals.