Job purpose Data Scientist is responsible for performance analysis, through data science, machine learning, and advanced statistical analytics techniques in the areas of payments, collections, operations, and companywide cross-functional projects. This position will utilize advance statistical skills to analyze data, portfolio performance trends, and forecasting skills to support credit risk functions. Duties and responsibilities
Create predictive models using large datasets to optimize credit risk and marketing campaign performance.
Create quantitative models and related components such as probability of default (PD), loss given default (LGD) and exposure at default (EAD) used for the Current Expected Credit Loss (CECL) estimates.
Develop sensitivity and scenario analysis to estimate the impact of macro-economic variables on expected credit losses.
Apply innovative and scientific/quantitative analytical approaches to draw conclusions and make recommendations to answer business objectives and drive change.
Develop statistical, econometric, AI/ML, and optimization models for Credit risk, Collections and Portfolio Management and execute the models in production.
Experience with full model lifecycle, agile and experimental design methods.
Develop analytics regarding loan performance, including delinquency and credit losses, identifying key drivers and developing forecast expectations.
Utilize advanced statistical analytics to assess future risk, opportunities, and effectiveness and translates results into meaningful solutions to enhance decision making.
Conduct ad hoc research projects incorporating project design, data collection and analysis, summarization of findings, and presentation of results.
Apply advanced knowledge to produce advanced analytical material for discussions with cross-functional teams to understand complex business objectives and influence solution strategies.
Interact with stakeholders to understand their business questions, crafting the methodology, to mine/analyze datasets and delivers a final insightful recommendation to stakeholders.
Master’s degree in Finance, Accounting, Statistics, Economics, Mathematics or other quantitative disciplines.
Minimum of 2 years of experience in quantitative role.
Experience in data mining, modeling and analyzing analytic.
Experience in Current Expected Credit Loss (CECL) accounting standard.
Experience in programming languages; Python, R, SAS and SQL.
PhD in Finance, Accounting, Statistics, Economics, Mathematics or other quantitative disciplines.