Develop automated systems and tools - such as scalable machine learning pipelines and fraud mitigation frameworks - that empower the organization to combat fraud efficiently and at scale Collaborate with cross-functional partners across Product, Platform, Machine Learning, and Operations to drive impactful strategic initiatives. Bachelor's degree in a relevant quantitative field (e.g., Industrial Engineering, Information Systems Engineering, Statistics) with 3+ years of applied data science/analytics experience, or a Master's degree with 2+ years of experience. Strong analytical and problem-solving skills with a data-driven mindset Strong understanding of optimization techniques and decision-making frameworks Experience in predictive modelling techniques such as Regression, Classification and Time series forecasting Practical Knowledge of data structures and algorithms as applied to large-scale data mining Proficiency in Python for data analysis and developing algorithmic solutions Experience working with large datasets, querying (e.g., SQL), and data manipulation Subsidiary: Analyze complex fraud-related problems and utilize data science and algorithmic thinking to uncover impactful opportunities and develop data-driven solutions Drive end-to-end projects from ideation to implementation, while balancing automation goals with business objectives Collaborate with cross-functional teams (engineering, analytics, product, operations) to translate business needs into scalable data science tools Engage stakeholders throughout project lifecycles by understanding business needs and aligning on priorities, impact, and implementation Support strategy discussions by providing actionable insights derived from exploratory data analysis, reporting and data-driven storytelling