Are you looking for an exciting opportunity to join a dynamic and growing team in a fast paced and challenging area? This is a unique opportunity for you to work in our team to partner with the Business to provide a comprehensive view.
 

As Risk Modeling - Machine Learning Associate in CCB Risk Modeling team, you will develop and implement machine learning models/ statistical models/ segmentations/strategies, leveraging big data and distributed computing platforms, with applications in risk management for its credit card and small business portfolio. The successful candidate will drive long term profitable growth with strong business acumen, collaborate in a team environment, and effectively communicate results to senior management. JP Morgan Chase (NYSE: JPM) is a leading global financial services firm with operations worldwide. The firm is a leader in investment banking, financial services for consumers and small business, commercial banking, financial transaction processing, and asset management. A component of the Dow Jones Industrial Average, JPMorgan Chase & Co. serves millions of consumers in the United States and many of the world's most prominent corporate, institutional and government clients under its J.P. Morgan and Chase brands. Information about JPMorgan Chase & Co. is available at http://www.jpmorganchase.com/.

 

Job responsibilities :

Utilize novel approaches to design and develop sophisticated machine learning models to drive impactful decisions for the business. Leverage big data/distributed computing/cloud computing platforms to optimize and accelerate model development processes. Work closely with the senior management team to develop ambitious, innovative modeling solutions to cater business need and deliver them into production. Collaborate with various partners in marketing, risk, technology, model governance, fair lending etc. through the modeling lifecycle (conceptualization, development, review, deployment, model usages and model maintenance) 

Required qualifications, capabilities, and skills

Ph.D. or MS degree in Mathematics, Statistics, Computer Science, Operational Research, Econometrics, Physics, or other related quantitative fields Deep understanding of advanced machine learning algorithms (e.g. random forest, XGBoost, Neural Networks etc) as well as design and tuning procedures Effective communication  Minimum 3 years of experience in developing and managing predictive risk models in financial industry. Fundamental understanding of consumer lending business.  Demonstrated experience in designing, building, and deploying production quality machine learning models.  Minimum 2 years of experience and proficiency in coding (Python, Tensorflow or PyTorch, PySpark, SQL), familiarity with cloud services (AWS Sagemaker, Amazon EMR)  Demonstrated expertise in data wrangling and model building on a distributed Spark computation environment (with stability, scalability and efficiency). Strong ownership and execution skills, proven experience of implementing models in production. Ability to collaborate with multiple stakeholders on projects of strategic importance. 

Preferred qualifications, capabilities, and skills

Experience in interpreting deep learning models is a plus.  GPU experience is a plus.