Machine Learning Engineer

What You’ll Do • Redesign and optimize MLOps and decision platform for fraud detection. • Architect large-scale big-data infrastructure to enable use of cutting-edge machine learning models for real-time fraud prevention. • Collaborate with data scientists and platform engineers to automate workflows. • Provide solutions that ensure compliance, security, and maintainability across the fraud detection ecosystem. • Work with high-dimensional datasets and leverage tools like Python, PySpark, and BigQuery to develop robust workflows for fraud signal detection. • Standardize rules and decision processes while enabling dynamic rule updates and analytics within the fraud detection platform. • Collaborate across multidisciplinary teams in engineering, product development, and data science to scale solutions globally. Story Behind the Need – Team & Key Projects • Team: Work closely with Core Automation team’s tech leads/managers. • Rebuild next-generation risk platform (NGRP) with engineering teams. • Be part of the pioneer team launching NGRP. Typical Day in the Role Daily Responsibilities: • Tasks distributed via Jira board (sprint planning/grooming cycle). • Regular standups (at least twice a week; daily if needed). • Onboarding requires more interaction with Eng/Product/US Risk core teams; later, ~50/50 split. • Work primarily within Jira for task assignments and tracking. • Code maintained in centralized GitHub repo. • Documentation in wiki pages or SharePoint/shared drives. Compelling Story & Candidate Value Proposition • Why this role is interesting: Design and implement scalable solutions to optimize fraud detection systems, spanning model development, feature engineering, and rule-based systems. • Collaborate closely with cross-functional teams to set a new global standard for efficacy and innovation. • Address critical business challenges, develop advanced automation frameworks, and integrate cutting-edge machine learning techniques to enhance decision-making capabilities. Candidate Requirements • Top 3 Must-Have Skills (stack-ranked): 1. BigQuery, Python 1. Understanding of production systems architecture and offline data overview 1. Machine Learning experience • Nice to Have: Risk and fraud experience or business acumen; experience working directly with engineers. • Years of Experience: 6+ • Degrees/Certifications: Master’s preferred; solid BS acceptable (STEM only). Compensation: $60-$65/hour