JPMorgan Risk Program Senior Associate - Card Merchant 

United States, Delaware, Wilmington 

13452579

Job Responsibilities

• Design and develop machine learning models to drive impactful credit decisions for the card business throughout the credit card lifecycle (e.g., acquisition, account management, transaction authorization, collection).
• Leverage cutting-edge machine learning techniques, including deep learning architectures on big data platforms with key emphasis on interpretability and replicability of such techniques.
• Work closely with the senior management team to develop ambitious, innovative modeling solutions and implement them in production to drive significant business impact.
• Collaborate with various business partners in marketing, risk, technology, model governance, compliance etc. throughout the entire modeling lifecycle. (development, review, deployment and ongoing monitoring)
• Present model result and ad-hoc research to senior leaders.

Required qualifications, capabilities and skills

• Ph.D. or Master’s degree from an accredited university in a quantitative field such as Computer Science, Mathematics, Statistics, Econometrics, or Engineering.
• Exceptional coding skills with at least one-year professional experience in coding (e.g. Python, SAS, Spark, Scala, or Tensorflow) and big data platform (e.g., Hadoop, HDFS, Teradata, snowflake, AWS cloud, Hive) .
• Solid understanding of advanced statistical methods and machine learning techniques: GLM/Regression, Random Forest, Boosting Trees, Neural Network, Clustering, KNN, Anomaly Detection etc.
• Strong ability to interpret and form a coherent story with complex data and communicate to a wide range of audience with various degree of technical acumen including senior leadership and executives.
• Advanced problem-solving skills and exceptional analytical skills.

Preferred qualifications, capabilities and skills

• Experience in credit card industry with strong business acumen.
• Experience in interpreting / explaining machine learning models such as XGBoost, GBM etc.
• Strong ownership and execution; proven experience in implementing models in production.
• Expertise in data wrangling and model building on a distributed Spark computation environment.