Make your mark on recognizing and communicating meaningful trends/patterns in data and reporting, identifying and validating data sources, and advising business and technology partners on data-promoted opportunities to increase efficiency and improve customer experience.

Working for Chase Home Lending data modeling team provides the opportunity to be exposed to different aspects of the business (from marketing, to originations, and servicing) to understand the linkage between them, and make creative machine learning models and reports that will have a real life measurable impact on customer experience and the business.

Job Responsibilities:

Recognizes and communicates meaningful trends/patterns in data and reporting Identifies and validates data sources and serving as a data expert and consultant to the predictive modeling team Advises business and technology partners on data-driven opportunities to increase efficiency and improve customer experience  Interfaces proactively with, and gathering information from, other areas of the business (Operations, Technology, Finance, Marketing) Extracts data from various sources and technologies using complex SQL queries 

Required qualifications, capabilities and skills:

Understand the problem statements, find the best course of action, and be creative. Collaborate with the business, developers, managers, and other stakeholders to implement the models, deliver the analytics, and integrate the new discoveries into existing models. Learn quickly about different aspects of the business as well as different modeling methods. Evaluate and monitor the performance of the new and/or existing models. Continuously improve the models and automate the reports and procedures. Explore new data, techniques, and environments in order to enhance current models and processes. Articulate the model results and analytical findings in concise and clear manner using presentations and visualizations.

Preferred qualifications, capabilities and skills:

Advanced degree in analytical fields (e.g. Computer Science, Data Science, Engineering, Statistics, Data Analytics, Applied Mathematics) Working knowledge on both supervised and unsupervised machine learning algorithms. Experience working with XGBoost, Random Forest, and Deep Learning. Experience with various modern analytics and data tools specially Python and SQL. Experience in AWS, R, Alteryx, SPSS Modeler, or SAS. Strong knowledge and experience on model evaluation methods and techniques.  Being excellent in teamwork, problem solving, and out-of-the box thinking. Experience with data visualization techniques and big data technologies. Excellent written and oral communication and presentation skills with experience on documenting the models and procedures in an organized manner.