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Team Overview
The Data Science Credit Product team within Global Technology and Data Analytics leads the development of attributes, scores, and similar products for TransUnion markets outside the United States. As a leader in Data Science & Analytics, the Director leads multiple teams of experienced managers and analytics professionals in the design, development, and implementation of solutions and products. The Director will report to a Vice President of Data Science & 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
- Independently execute a broad range of people-related responsibilities, including talent acquisition, pipeline management, training, development, goal setting, and performance management.
- Lead business functions including revenue planning, budgeting, capacity planning, resource allocation, project management, work measurement, management reporting, and merger and acquisition support.
- Proactively contribute to the development of operating policies and procedures of significant scope and complexity to advance operational excellence across Data Science & Analytics.
- Ensure the quality and continuous improvement of analytical product delivery and deliverables.
- Maintain the right team composition and use a mature approach to managing direct reports and influencing stakeholders while supporting team members’ careers.
- Ensure team members are regularly upskilled to meet the organization’s changing needs.
- Represent the team with global and GCC stakeholders, identify the drivers of team success, and independently advance them while securing stakeholder alignment.
- Availability to travel internationally up to 50%.
Required Knowledge and Experiences
- Experience managing a team of 20+ people, with a demonstrated ability to take on additional responsibilities over time.
- Deep knowledge and hands-on expertise in predictive modeling, statistical inference, machine learning, and other advanced analytical techniques.
- Deep, current knowledge of industry trends and dynamics, with the ability to speak authoritatively with customers about topics relevant to their business.
- Ability to collaborate with customers to frame and define ill-structured problems and design effective solution approaches.
Broad understanding of global analytics services and customer demands and substantial experience delivering formal presentations to large and senior-level groups, as well as presentations in smaller, less formal settings.
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Benefits that support every part of your life:
At TransUnion, we design benefits to help you feel well, do well, and plan well—from day one.
For Your Health : Enjoy day-one eligibility for medical, dental, and vision coverage, plus supplemental plan options. Spousal, domestic partner, and other eligible dependent coverage is available on select plans. Choose tax‑advantaged HSA and FSA accounts to make everyday care more affordable.
For Your Protection : We’ve got your back with company‑paid basic life and AD&D, optional voluntary life and AD&D for you and your family, and short‑ and long‑term disability. You can also opt into a legal plan, pet insurance, and travel accident coverage.
For Your Family : From adoption assistance and fertility planning coverage to caregiver support, we’re here for every chapter. Access Dependent Care FSA for possibility of an employer match, a complimentary Care@Work membership, and up to 12 weeks of paid parental leave with eligibility for a thoughtful, gradual return.
For Your Future : Build toward what’s next with our 401(k) with employer match and Employee Stock Purchase Plan (ESPP). Tap financial wellness resources, career coaching, and optional long‑term care insurance to plan confidently.
For You : Grow and recharge with tuition reimbursement, flexible time off for exempt employees or paid time off for nonexempt employees, up to 12 paid holidays per year, commuter benefits, employee discounts, charitable gift matching, and paid volunteer time off, plus corporate volunteer events that make it easy to give back.
For Your Wellness : Access 24/7 support including professional therapy, coaching, and emotional well‑being programs alongside guided meditation and resources that support physical, mental, social, and financial wellness.
We are committed to being a place where diversity is not only present, it is embraced. As an equal opportunity employer, all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, age, disability status, veteran status, genetic information, marital status, citizenship status, sexual orientation, gender identity or any other characteristic protected by law. Additionally, in accordance with Section 503 of the Rehabilitation Act of 1973 and the Vietnam Era Veterans’ Readjustment Assistance Act of 1974, TransUnion takes affirmative action to employ and advance in employment qualified individuals with a disability and protected veterans in all levels of employment and develops annual affirmative action plans. Components of TransUnion’s Affirmative Action Program for individuals with disabilities and protected veterans are available for review to any associate or applicant for employment upon request by contacting [email protected] .
Qualified applicants with arrest or conviction records will be considered for employment in accordance with applicable law, including the Los Angeles County Fair Chance Ordinance for Employers, the San Francisco Fair Chance Ordinance, Fair Chance Initiative for Hiring Ordinance, and the California Fair Chance Act.
Adherence to Company policies, sound judgment and trustworthiness, working safely, communicating respectfully, and safeguarding business operations, confidential and proprietary information, and the Company’s reputation are also essential expectations of this position.
Pay Scale Information:
The U.S. base salary range for this position is $150,100.00 - $225,000.00 annually. *The salary range for this position reflects a reasonable estimate of the range of compensation for this job. At TransUnion, actual compensation is based on careful consideration of additional factors such as (but not limited to) an individual’s education, training, work experience, job-related skill set, location, and industry knowledge, as well as the scope and responsibilities of the position and market considerations. Regular, fulltime non-sales positions may be eligible to participate in TransUnion’s annual bonus plan. Certain positions may be also eligible for long-term incentives and other payments based on applicable company guidance and plan documents.
 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's Internal Job Title:
Director, Data Science and Analytics Company:
TransUnion LLC
Salary Context
This $150K-$225K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At TransUnion, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills in Demand for This Role
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $211,950 based on 13,048 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,300. This role's midpoint ($187K) sits 12% below the category median. Disclosed range: $150K to $225K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
TransUnion AI Hiring
TransUnion has 3 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Chicago, IL, US, Bengaluru, Karnataka, India. Compensation range: $112K - $225K.
Location Context
AI roles in Chicago pay a median of $193,440 across 429 tracked positions. That's 9% below the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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