My client is looking to fill an AVP level role on their Collections Strategy & Analytics team in which you will be responsible for developing, delivering, and monitoring statistical segmentations and related strategies for traditional and alternative contact channels using advanced analytics and predictive statistical tools. As an AVP you will provide thought leadership in the team, initiate and lead projects, and work directly with stakeholders throughout the process. Candidates should have 3+ years of experience in data analytics to support strategic decisions (preferably in credit risk or collection strategy) along with proficiency in SQL, SAS, Data mining and E-miner or similar decision tree software.
- Develop, test and rollout strategy changes for pre-delinquent (current accounts that have missed a payment) and pre-charge off (delinquent) accounts.
- Build new decision tree segmentations using CHAID/CART, conduct analysis, develop strategy proposals, obtain approvals, partner with multiple teams to implement changes, and assess post-implementation execution and strategy performance
- Monitor and evaluate the performance of existing segmentations
- Use optimization techniques to help maximize impact of segmentation strategies
- Assess attributes from internal data sources, partners, and external data sources to determine if the new attributes are incrementally predictive vs. champion segmentation
- Ability to provide analytical support including pulling data, preparing analysis, interpreting data, making strategic recommendations, and presenting to various audiences
- Co-lead or support cross functional project teams to address strategy and performance impact
- Ensure strategy documentation is comprehensive, accurate and up to date (i.e., audit ready)
- Bachelor's or equivalent degree in a quantitative field (Master's preferred)
- 3+ years of experience in data analytics to support strategic decisions
- Proficiency in advanced SQL, SAS, Data mining and E-Miner / similar decision tree software
- Credit / Risk Management or Collection Strategy experience
- Experience with segmentation techniques such as decision trees or k-means clustering
- Ability to work with large or complex datasets
- Experience with model development techniques such as linear regression, logistic regression, random forest, etc.
- Experience with Data Lake
- Experience working with cross-functional project teams