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Global brand, mid-level ML role in metro with broad skillset requirements increases candidate competition.
Requires ML productionization and LLM/MLOps expertise, transferable but favors candidates with financial services or risk experience.
Specific years, mandatory ML productionization, LLM and GCP/MLOps skills create stringent shortlisting filters.
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Own the development, deployment, and automation framework for credit risk models and analytic solutions serving TransUnion’s clients.
Design and implement data extraction, pipeline setup, and statistical analysis programs on large datasets using R, Python, SQL, Hive, Spark on cloud platforms.
Build AI automation workflows using LLMs and create APIs for enterprise integration to deliver analytic solutions internally and externally.
Bachelor's degree in statistics, applied mathematics, financial mathematics, engineering, operations research, or a related quantitative field.
At least 4 years of professional experience in AI/ML analytic roles; advanced analytic experience in Financial Services or related industry preferred.
Proficiency in programming languages such as R, Python, SQL, Hive; experience with Google Cloud Platform, Vertex AI, Cloud Run, and familiarity with Java/C++/.NET integration.
Experience with ML model productionization, hands-on exposure to LLMs, prompt engineering, agents, and RAG; hybrid work mode with minimum 2 days per week at office.
Experienced in credit risk modeling and analytics within financial services or consumer credit domain with familiarity of credit bureau data and business practices.
Capable of independently managing complex, matrixed projects involving AI automation and production workflows in a global, fast-paced environment.
Skilled in using and integrating modern big data frameworks (Hadoop, Spark) and MLOps tools, with a strong focus on AI adoption and operational scalability.