Staff Data Scientist

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Team Overview

This role reports into a Senior Manager of Data Science & Analytics, joining the Financial Services Consulting team. The Financial Services Consulting team at TransUnion is an industry recognized, client-facing department that rewards an entrepreneurial spirit. We have deep technical expertise and an established reputation as an analytic solutions provider in the Financial Services industry. 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

• Analytics & AI Consulting Delivery - Lead complex analytic and AI-enabled consulting engagements from opportunity shaping through solution design, model development, evaluation, deployment readiness, and ongoing monitoring. Translate client needs into practical, scalable solutions that deliver measurable business value.
• Technical Leadership & Modern AI/ML - Provide hands-on guidance across machine learning, statistical modeling, generative AI, large language models, retrieval-augmented generation, agentic workflows, model evaluation, MLOps, monitoring, and responsible AI/model governance. Help formalize reusable assets, documentation standards, and repeatable delivery practices.
• Client & Commercial Impact - Own analytic consulting responsibilities for assigned clients, identify strategies and opportunities to test and adopt TransUnion analytic products and services, and partner with Sales, Product, Technology, and vertical support teams to shape differentiated, data-driven recommendations.
• Insight Communication - Deliver analytic insights, model considerations, risks, and recommendations in concise and compelling presentations for internal and external stakeholders across a range of technical and business backgrounds.
• Research, Innovation & Process Improvement - Contribute to research and innovation initiatives in collaboration with DSA peers and cross-functional partners, including applied AI, responsible AI, experimentation, and emerging analytic techniques. Identify opportunities to improve quality, efficiency, governance, and scalability of analytic delivery.
• Peer Leadership & Team Development - Mentor, coach, and train junior colleagues and peers by sharing expertise, clarifying expectations, providing candid and constructive feedback, recognizing strong contributions, and helping foster an inclusive, high-performance culture.
• Ability to travel 10-20% of the time.

Required Knowledge and Experiences

• Master’s or PhD degree in statistics, applied mathematics, financial mathematics, computer science, engineering, operations research, or another highly quantitative field; or a Bachelor’s degree in a quantitative field.
• 5+ years of demonstrated success directly supporting internal or external clients and 6+ years of professional experience performing analytic work, preferably in industries served by TransUnion. These experiences may or may not overlap.
• Advanced programming, data engineering, and applied AI/ML skills, including proficiency with Python and/or R, SQL, cloud-based analytics environments, distributed data platforms, and modern machine learning frameworks.
• Demonstrated ability to lead complex analytic and AI-enabled solution engagements under limited supervision, including solution design, model development, validation, stakeholder alignment, and delivery coordination.
• Aspiring leadership behaviors, including self-awareness, learning agility, inclusive collaboration, peer coaching, accountability, change leadership, and the ability to motivate and guide others toward successful team outcomes.

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 with generative AI, large language models, retrieval-augmented generation, agentic workflows, model evaluation, MLOps, model monitoring, and responsible AI/model governance.
• Familiarity with credit bureau data, regulated financial services environments, credit risk lifecycle use cases, fraud, insurance, or digital marketing analytics.