Team Overview
The Data Science & Analytics team delivers advanced analytics, data science, and consulting solutions across multiple industries including financial services, retail and insurance. The team leverages big data, cloud computing, machine learning, and predictive analytics to develop innovative products, enhance existing solutions, and provide analytics-driven insights to clients. The team also supports consultancy-led sales engagements, scorecard development, portfolio analytics, affordability solutions, and research initiatives while collaborating with global analytics experts and cross-functional stakeholders. This team operates within the Global Data Science & Analytics organization. This role will be rolled up under Product Analytics Leadership.
This is a hybrid position and involves regular performance of job responsibilities virtually as well as in-person at an assigned TU office location for a minimum of two days a week.
Role Overview and Core Responsibilities
•Develop predictive models, analytical solutions, and business intelligence capabilities for clients across various industries.
•Participate in descriptive, predictive, and prescriptive analytics engagements supporting product and client development initiatives.
•Design and develop programs for data extraction, segmentation, statistical analysis, machine learning, and AI-based solutions using R and Python.
•Analyze large-scale datasets and generate data-driven recommendations and insights.
•Conduct scorecard development, affordability analytics, portfolio reviews, performance monitoring, and ad hoc analytical studies.
•Support bureau data submissions and retrospective analysis activities.
•Collaborate with project managers, analytics teams, and business stakeholders to ensure timely delivery of analytical workstreams.
•Present analytical findings and recommendations to internal and external stakeholders.
•Prepare and maintain analytical deliverables and supporting documentation to required quality standards.
Required Knowledge and Experiences
•Bachelor's degree in Statistics, Mathematics, Business Analytics, Data Science, Economics, or related quantitative discipline. Equivalent professional experience in analytics, risk, or model governance will also be considered.
•Experience in predictive modeling, statistical analysis, and data science techniques.
•Strong programming skills in R and/or Python.
•Knowledge of machine learning, artificial intelligence, data extraction, and data segmentation methodologies.
•Experience working with large datasets and analytics solutions.
•Understanding of scorecard development, portfolio analytics, and credit risk analytics is preferred.
