CNH Industrial is a global leader in industrial goods. We provide farmers with cutting-edge technologies to help them feed a growing world population and we assist in building and rebuilding cities and infrastructures, all with future-proof powertrain solutions. With our Case IH, New Holland Agriculture, Case and New Holland Construction, and FPT Industrial brands, and comprehensive solutions for financing and aftermarket services, we're driven to meet the needs of our customers.
As Credit Risk Data Scientist, you will join our Global Risk team in CNH Industrial Financial Services. As a member of our global team, you will be involved with projects of other regions, supporting data science standards, ad hoc and periodical risk-centric projects with focus on preparing, managing, and analyzing multiple data sets, applying machine learning algorithms to create credit risk models, as well as following established policies and procedures for developing and distributing standard reports. This team member will collaborate with other risk team members and stakeholders from across the organization to initiate and implement creative business solutions which meet business needs. To be successful in this role, you must have very strong quantitative, logical, analytical and problem-solving skills, being able to work with little supervision in a fast-paced environment, and to handle multiple projects simultaneously.
This position follows a HYBRID work model from our Racine, WI office
The annual salary range is $85,000.00 - $115,000.00
- Apply Machine Learning (ML) techniques on big data to d evelop, refine and implement retail/commercial lending models as well as associated alignments and cutoffs for all regions. Models included but not limited to: origination, behavior, collection, financial, bureaus, delinquency, fraud, losses and auto-approval
- Work with IT to create/improve and keep structured data, p erform data maintenance and management on daily basis, and consult on best practices for data retention
- Work to strengthen/increase partnership with external data suppliers worldwide, expanding our options to improve our models prediction power
- Assess tools/data purchased to support risk management plus tools used by outside vendors or partners
- Perform ad hoc analyses of business situations, systems, issues and problems as well as research and test new technologies for risk mitigation
- Provide and present the results of analyses in the form of graphs, charts, and tables, in high quality fashion and acceptable format, for management, peer and audit reviews
- Document and maintain all procedures and models steps
- Validation and monitoring of risk tools and their application in underwriting, collection or other user areas
- Bachelors degree in a quantitative discipline or related field (e.g. Analytics, Data Science, Statistics, Mathematics, Economics, Finance, Engineering, etc.)
- 2+ years of experience creating/applying advanced algorithms and/or statistical models such as (machine learning techniques, Logistic regression, hazard rate, time series, Simulation, etc.)
US applicants: CNH Industrial is an equal opportunity employer. This company considers candidates regardless of race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or veteran status. Applicants can learn more about their rights by viewing the federal "EEO is the Law" poster and its supplement here
. CNH Industrial participates in E-Verify and will provide the federal government with your Form I-9 information to confirm that you are authorized to work in the U.S. You can view additional information here.
If you need reasonable accommodation with the application process, please call 1-800-889-4422 option 1 and then option 5, or contact us at firstname.lastname@example.org.
Read about our company's commitment to pay transparency by clicking this link: pay transparency notice
Canada applicants: CNH Industrial is an equal opportunity employer. This company considers candidates regardless of race, colour, religion, sex, sexual orientation, gender identity, nationality, place of origin, disability, marital status, family status, age, or any other ground prohibited by applicable provincial human rights legislation.