Data Science

Team Overview

The Financial Services Analytics team partners with banks, lenders, insurers, and other financial institutions to solve complex business challenges using advanced analytics and TransUnion data assets. The team delivers predictive modeling, risk analytics, fraud solutions, and business intelligence insights that help clients make smarter business decisions and drive measurable outcomes. This role reports to Director of Data Science and Analytics.

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 and advanced analytics solutions for clients across credit risk, fraud, insurance, and marketing use cases.Lead analytics engagements from project design and development through implementation, validation, and client presentation.Partner with internal stakeholders, sales teams, and external clients to identify opportunities where data science can create business value.Conduct descriptive, predictive, and prescriptive analyses to solve complex customer business challenges.Translate analytical findings into actionable business recommendations and present results to technical and non-technical audiences.Design, build, and optimize data extraction, segmentation, and statistical analysis processes using large-scale datasets.Create repeatable analytical frameworks and process improvements that increase efficiency and scalability.Support business development efforts through project proposals, client presentations, and analytic consulting.Required Knowledge and Experiences

Master’s degree or PhD in Statistics, Applied Mathematics, Financial Mathematics, Computer Science, Engineering, Operations Research, or another highly quantitative field.1+ years of experience performing analytics, data science, statistical modeling, or quantitative analysis.Understanding of U.S. financial services risk management practices and how data-driven insights support business decisions.Experience within financial services, insurance, fraud, credit risk, digital marketing, or related industries.Required Technical Skills

Python or R for statistical analysis, predictive modeling, and data science solution development.SQL for data extraction, transformation, segmentation, and analysis.Experience working with large datasets and distributed data platforms such as Hive and Spark.Knowledge of machine learning, predictive analytics, statistical modeling, and business intelligence techniques.We’re also looking for the preferred skills below. Whether you are proficient or could use some brushing up, we’re happy to support your career development and growth in:

Experience working with credit bureau data, credit risk models, or financial services analytics.Exposure to programming languages such as Java or C/C++.Experience in client-facing consulting or analytics advisory engagements.Knowledge of fraud analytics, insurance analytics, or marketing analytics solutions.