Analyst, Data Science & Analytics

TransUnion's Job Applicant Privacy Notice

Team Overview

At TransUnion, we're building the next generation of Fraud Prevention, Identity, and Marketing solutions by combining AI, automation, cloud engineering, and large-scale data platforms.

Join a high-impact engineering team within our Data & Analytics organization, developing intelligent systems that help businesses detect fraud, improve customer experiences, automate decision-making, and unlock insights from data at scale.

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

Role Overview and Core Responsibilities

This position is responsible for supporting the development of fraud risk management and business intelligence analytic solutions serving TransUnion’s clients.

• You will partner with state-of -the-art Fraud Analytics team to create cutting edge fraud solutions, extensively using graph technology to combat fraud.

• You will work on unique data sets with petabytes of volume to analyze and build meaningful model solutions.

• You will deliver analytic insights and recommendations in succinct and compelling presentations for internal and external customers and an executive audience.

• You will identify strategies and opportunities for customers to test and adopt TransUnion’s analytic products and services.

• You will foster a high performance culture and cultivate an environment that promotes excellence and reflects the TransUnion brand.

• Bachelors (4 year) degree in statistics, applied mathematics, financial mathematics, engineering, operations research, or another highly quantitative field. And, at least one (1) year of professional experience performing analytic work in SaaS, Consulting or related industries.

• Worked with advanced machine learning methods.

• Fluent in Python and PySpark

• Passionate about LLMs, advanced analytics and data.

Required Knowledge and Experiences

• Master’s (2 years) in statistics, applied mathematics, financial mathematics, engineering, operations research, or another highly quantitative field.  And, at least one (1) year of professional experience performing analytic work in Financial Services or related industries

• Using Machine Learning, Deep learning or graph based approaches for modelling fraud risk.

• Has experience on any of the Cloud based tools : GCP/AWS/Azure using large datasets in distributed environment

• Experience or familiarity with unsupervised modelling techniques

• Experienced working in a company with a global presence

• Well versed with NLP techniques - from Statistical approaches to Transformers and exposure to LLMs

Required Knowledge And Experiences

Required Technical Skills

• Bachelors (4 year) degree in statistics, applied mathematics, financial mathematics, engineering, operations research, or another highly quantitative field. And, at least one (1) year of professional experience performing analytic work in SaaS, Consulting or related industries.
• Worked with advanced machine learning methods.
• Fluent in Python and PySpark
• Passionate about LLMs, advanced analytics and data.

TransUnion Overview:

At TransUnion, we encourage and are committed to creating a real, positive impact and shared sense of purpose within our Workforce for Good, which empowers our people to grow, innovate and contribute to a better future for our communities and customers. We strive to build an environment where our associates are in the driver’s seat of their professional development— while having access to help along the way. We recognize that success comes when our associates thrive both professionally and personally; that’s why we prioritize work/life flexibility and offer resources for our teams across the globe to collaborate and drive excellence.

 

Be a part of our Workforce for Good – you’ll work with great people, pioneering products and cutting-edge technology.

TransUnion Job Title

Analyst, Data Science and Analytics