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Known global brand, metro location, popular data science title, and broad skillset increase applicant competition.
Credit-risk and credit-bureau familiarity preferred, so industry-specific experience strongly affects fit.
Requires quantitative degree and multiple technical skills but lacks firm years requirement, so moderate shortlisting filters.
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Develop and support credit risk management and business intelligence analytic solutions for TransUnion’s clients, focusing on predictive risk management for financial institutions.
Design, write, and execute data extraction, segmentation, and statistical analysis programs using languages such as R, Python, SQL, Hive, and Spark on cloud and server-based platforms.
Present analytic insights and strategic recommendations to internal teams, external clients, and executive audiences, while identifying opportunities to adopt TransUnion’s analytic products.
Bachelor’s degree in statistics, applied mathematics, financial mathematics, engineering, operations research, or a highly quantitative field.
Proficiency in statistical programming languages, especially R; experience with SQL, Python, Hive, Pig, C/C++, Java, and Microsoft Office is preferred.
Ability to travel 10-20%; hybrid work arrangement requiring minimum two days onsite per week at a TransUnion office.
Work Experience Required: Minimum 6 months in analytics or internships in Financial Services or related industries preferred but not strictly mandatory.
Experienced in credit risk analytics with familiarity of credit bureau data, ideally with some exposure to financial services.
Proficient in big data technologies (Hadoop, Spark, cloud computing environments) and data visualization tools like Tableau.
Capable of managing multiple analytic projects independently in a complex, matrixed environment with cross-functional collaboration globally.