## 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.**