What you’ll be doing (ie. job duties):

📍Leverage your fraud and risk domain knowledge to assist our machine learning teams with feature selection and model optimization, reducing false positive rate while maintaining fraud reduction. 📍Engineer new features, evaluate and improve the data quality for our existing features. 📍Evaluate offline model performance, help us build out real time performance monitoring. 📍Proactively identify and close gaps in our model architecture. 📍Work with internal stakeholders in Product and Engineering to model requirements for international expansion and new product addition. 📍Use data to make risk decisions impacting large segments of our customer population; effectively measure and assess associated tradeoffs using quantitative analysis. 📍Synthesize data learnings into compelling stories and communicate them to Payments Risk leadership.

What we look for in you (ie. job requirements):

📍4+ years relevant experience. 📍2+ years evaluating machine learning model performance and creating features to be consumed by machine learning models. 📍2+ years of experience in data analysis, working with databases, and querying (e.g., SQL, MySQL, etc). 📍Exposure to modern ML techniques (e.g., DNNs, transformers, LLMs, etc.). 📍Experience working with quantitative tools such as Python or R. 📍BA / BS degree or equivalent practical experience. 📍A passion for fighting fraud and abuse, and the curiosity to self-drive investigations, identify patterns, and find the root cause. 📍Be able to independently create plans for analytics projects and build collaboration with cross-functional teams. 📍Demonstrate our core cultural values: clear communication, positive energy, continuous learning, and efficient execution

If you’re passionate about blockchain and decentralized technologies, explore more opportunities in web3 and cryptocurrency careers.