Senior Quant Analytics Associate - Fraud Risk

If you are passionate about leveraging advanced analytics and AI to combat fraud and drive business value, we encourage you to apply!

As a Senior Quantitative Analytics Associate in our Fraud Risk team, you will help prevent plastics fraud through advanced, data-driven analysis.

You'll gain a comprehensive understanding of the point-of-sale transaction lifecycle and deliver timely, efficient, and tailored solutions.

You will collaborate with cross-business partners to leverage advanced analytics for fraud/scam prevention, dispute and claim management, and optimization of risk/reward tradeoffs (losses/OpEx/customer experience), with the goal of driving positive business outcomes.

Job Responsibilities


* Analyze large datasets to detect patterns, trends, and anomalies indicative of fraudulent activity.


* Build, develop, and maintain reporting and data automation systems to communicate insights to leadership for strategic decision-making.


* Enhance internal analytical techniques and introduce best practices to improve key business metrics.


* Work independently and collaboratively with cross-functional partners, from problem identification to data analysis and delivering actionable recommendations.


* Develop and implement GenAI and Agentic AI solutions using Python to automate and optimize decision-making processes.


* Apply large language models (LLMs), machine learning (ML) techniques, and statistical analysis to improve decision-making and workflow efficiency across fraud operations and customer experience.


* Design and demonstrate proof-of-concepts (POCs) for extracting insights from structured and unstructured data using advanced analytics; build and iterate on prototype solutions.


* Stay current with the latest research in LLM, ML, and data science, and leverage emerging techniques for ongoing enhancement.

Required Qualifications, Capabilities, and Skills


* Advanced degree in a quantitative discipline (e.g., Computer Science, Mathematics, Operations Research, Data Science).


* 3+ years of experience in Risk Management or any quantitative field


* Hands-on experience with SQL, Python, and Alteryx.


* Strong understanding of the foundational principles and practical implementation of machine learning algorithms for anomaly detection, including clustering, classification, neural networks, distance-based, and time series methods.


* Experience creating generative AI solutions using LLM prompt engineering and Retrieval Augmented Generation (RAG).


* Experience with evaluation metrics for ML and generative AI.


* Demonstrated ability to communicate complex concepts and results to both technical and business audiences.

Preferred Qualifications, Capabilities, and Skills


* Hands-on experience with behavioral and transactional analytics tools and techniques.


* Familiarity with model explain ability and self-validation techniques.


* Preferred experience supporting mor...