Job Responsibilities:
- Develop best in class data-driven credit policies and business strategies: Leverage external (e.g. banking, tax, financial, credit bureau and other data) and internal performance data to develop credit policies (e.g. underwriting criteria, dynamic credit limit programs, etc.). Minimize credit loss by developing and implementing appropriate processes and procedures to identify and mitigate high risk customers. Design and implement data-driven strategies to improve funnel metrics and credit quality for new customer originations. Work closely with the sales and business development teams to support business growth strategies that preserve effective underwriting and ensure the appropriate application of Jeeves’ credit policy
- Deliver advancements in data and analytics: Contribute to the development of a data and analytics framework to improve processing, underwriting, tracking, risk management, and reporting procedures. Design and execute data driven analyses and tracking procedures to enhance insights on credit risk for individual, and the portfolio of companies. Leverage predictive statistical methodologies (e.g., linear/logistic regression, segmentation analysis) to draw insights and develop business strategies. Partner with data scientists to build and leverage credit risk models to optimize credit policies and improve business performance
- Improve credit monitoring and governance: Build dashboards to monitor portfolio health and strategy performance. Develop and implement improvements to credit portfolio monitoring, client review tracking, management reporting, and customer risk rating assignments
Required Qualifications:
- 2+ years in analytics within credit risk management.
- Fluent in English, Jeeves is a global company and English is the language we use internally to communicate between regions.
- Excellent written and verbal communication skills.
- Intellectual curiosity.
- Ability to translate complex data and model results into actionable business strategies.
- Proficiency in SQL and Excel.
- Experience using Python or R for data analysis and statistical modeling (regression, clustering, etc.).
Preferred Qualifications:
- Experience in commercial credit risk, specifically within credit cards, payments, lending, or related industries.
- Experience utilizing alternative data sources.
- Experience in architecting, implementing, and interpreting risk/scoring models in conjunction with data science teams.
- Experience in high growth startups preferred.
- Experience with visualization tools such as Tableau.
- Experience building complex financial products and models
