DescriptionDoes working with real data and having an impact on the small businesses that are the backbone of the US economy excite you? Do you want to be a thought leader in the Payment space by stretching boundaries with innovative Fraud solutions ?
As an Applied Machine Learning Scientist in Trust & Safety for the Payments Organization, you will be involved in developing machine learning models that facilitate safe & secure SMB payments by detecting and mitigating Fraud Risk. You will experiment with various relevant Al & ML algorithms and techniques to build best-in-class solutions as part of the organization that oversees several trillion dollars of Wire/ACH transactions and hundreds of millions of card transactions.
J.P. Morgan Chase Corporate & Investment Bank is a global leader across investment banking, wholesale payments, markets and securities services. The world's most important corporations, governments and institutions entrust us with their business in more than 100 countries.
Job Responsibilities
- Build machine learning systems and models for detecting payment fraud, merchant fraud, and merchant risk
- Research and analyze large data sets using advanced exploratory techniques and communicate findings to key stakeholders
- Drive and own the complete lifecycle from data extraction, model development through model deployment and production evaluation/maintenance
- Design and Implement Knowledge graph capturing information from various 3rd party/partner data sources and relationships therein
- Collaborate closely with Business, operations and Product teams to devise effective Risk and Fraud solutions.
- Bring an AI/ML first thinking to our Fraud/Risk solutions and thus achieving operational excellence.
- Design and Implement Knowledge graph capturing information from various 3rd party/partner data sources and relationships therein
- Collaborate closely with Business, operations and Product teams to devise effective Risk and Fraud solutions.
Required Qualifications, Capabilities and Skills:
- MS or Ph.D. in Machine Learning, Data Science or related discipline, e.g. Computer Science, Applied Mathematics, Statistics, Physics, Artificial Intelligence
- In-depth understanding of machine learning and modeling algorithms such as decision trees, random forest, neural networks, graph models
- Technical expertise in data preprocessing, feature extraction, model building, and statistical analysis
- Proficiency in databases (SQL), and programming languages (at least one of the following: Python or Java)
- 3+ years experience with machine learning APIs and computational packages like XgBoost, Pandas, TensorFlow, Scikit-Learn, NumPy, SciPy
- 5 + years of experience with big-data technologies such as Hadoop, Spark, Flink.
Preferred qualifications, capabilities, and skills
- Past AI/ML experience in Payments is a big plus.