The Predictive Analytics Consultant will be a key member of the analytics team, responsible for leading the design and delivery of critical solutions like Automated Underwriting, Risk Scoring, and Portfolio Monitoring. This role involves utilizing advanced data science tools and techniques to provide analytics services that optimize decision engines and improve the lending operations of financial institutions. The position will focus on building, testing, validating, and deploying predictive and optimization models that support credit underwriting decisions. The consultant will develop data-driven solutions to enhance credit risk assessments, automate decision-making, and optimize underwriting processes.
The ideal candidate should possess a thorough understanding of loan origination systems, core underwriting practices, credit bureau data, and the interaction between these areas to drive predictive analytics. Additionally, they should have a strong grasp of how decisioning engines work in lending, banking, or credit union environments for consumer loans, including data integration and automated underwriting.
Responsibilities:
Managing large, complex datasets from multiple sources, ensuring they are accurate, clean, and organized for analysis. Perform detailed data wrangling tasks to handle data inconsistencies to prepare data for use in predictive models and analysis.
Implement advanced data transformation techniques (e.g., feature engineering, aggregation, normalization) to optimize data for specific machine learning, optimization and statistical models.
Work on various types of predictive models, including classification, regression, and clustering, using algorithms like decision trees, random forests, or neural networks.
Develop end-to-end analytical solutions, from data collection to model deployment, ensuring that the solutions meet the client's business objectives, such as improving lending strategies or underwriting decisions.
Qualifications:
Bachelor’s or Master’s degree in Statistics, Data Science, Analytics, Mathematics, Economics, Finance, or a related field is preferred
Expert-level skills in programming languages such as Python for model development and analysis leveraging Pandas, Scikit-learn, and other data handling, statistical, optimization, and machine learning frameworks
Proficiency in AWS for training, building, and deploying models is preferred, along with experience in MLOps.
