Description: Position Summary:Reason for contingent headcount? Replacement for an existing CW, possible to convert to full time What is the project(s) name? Credit Risk Loss Forecast model What are the key objectives for this resource (project summary)? Develop and Maintain Credit Risk Loss Forecast Model What specific TFS departments will they interact with? Risk, Accounting, Treasury, Finance Overview: The primary responsibility of this role is to provide data science support to Credit Risk Loss Forecast domain. The incumbent will balance hands on model development activities with providing direction to the business. Specific responsibilities include extracting and linking data from company systems, manipulating and cleaning the results, building predictive and prescriptive models to extract insights and to present the findings and recommendations to business partners and leaders across the business. Key Responsibilities:Turn Data into Actionable Analytical Insights
- Partner with the business to identify the most important questions that need to be answered about our customers and their value.
- Hands on data extraction and data wrangling from internal and external systems using SAS, SQL, Python, R, etc.
- Analysis and model building using advanced analytics tools and techniques using statistical software packages like SAS, Python, R, etc.
Presenting/Communicating Insights and Recommendations
- Develop, manage and deploy analytic solutions using statistical software packages like SAS, R, Python, etc.
- Build PowerPoint presentations to recommend business decisions.
- Create output and or results that visualize findings using fixed reports and or tools like Tableau/Power BI
Facilitate Project Planning
- Develop analytical project plans and integrate inflows and outflows of information with teams upstream and downstream from analytical efforts.
- Attend and participate (where appropriate) in various project planning and prioritization sessions
- Requirements: Requirements: A successful candidate will have:
- Advanced Degree. Specify: Master's Degree in Statistics, Econometrics, Finance, Operations Research, or equivalent. PhD is preferred
- Modeling development or model validation experience
- Work experience in SAS and SQL
- Proficiency in Python or R