Senior Modeler
Stellantis
- Dallas, TX
- Permanent
- Full-time
- Use quantitative and analytical skills to mine, process and analyze data using SAS, Python, SQL, MS Excel, and other business tools.
- Build, recalibrate, and validate advanced predictive or forecasting models.
- Support the development of presentations on performance, trends, and projections to upper management to facilitate business and strategic decisions.
- Prepare and distribute recurring and ad-hoc analysis on residual value performance and projections.
- Collaborate on the creation of residual value reports focusing on key performance indicators for Stellantis models.
- Provide easy to understand, action-oriented analytical reports and other presentations to the management team.
- Identify and evaluate trends with respect to the performance of our lease portfolio.
- Support the development of recurring reports of economic indicators and assumptions.
- Collaborate with team members and department leaders on execution of residual risk initiatives, assess performance, and create recurring reports on industry trends for key market indicators.
- Maintain confidentiality of personal information for consumers, including, but not limited to, Social Security numbers, and dates of birth.
- Performs other ad-hoc duties as assigned.
- Minimum 7 years data analytics experience.
- Minimum 3 years experience with risk management or pricing/optimization.
- Bachelor s or Master s degree in Mathematics, Statistics, Economics, Computer Science, Finance, or other quantitative fields. OR
- Master s degree in Mathematics, Statistics, Economics, Computer Science, Finance, or other quantitative fields with minimum 5 years experience in statistical or data analytics including minimum 1 year experience with risk management or pricing/optimization experience.
- PhD Degree in Mathematics, Statistics, Economics, Computer Science, Finance, or other quantitative fields with minimum 1 year experience in statistical modeling or data analytics.
- Strong oral and written communication and interpersonal skills.
- Ability to work with mathematical concepts such as probability and statistical inference, and to apply concepts to practical situations.
- Experience with statistical forecast modeling, SQL, and SAS.
- Experience with statistical analysis software/languages (SAS, SQL, Python, & R) in addition to training in statistical modeling, data analysis, MIS, or computer programing.