Top AI & Machine Learning Jobs

Lead exploratory AI research and innovation initiatives, evaluating emerging techniques such as neuro-symbolic AI, causal AI, agentic AI, reasoning systems, and knowledge-enhanced machine learning. Design prototypes, assess capabilities and risks, identify business applications, and collaborate with engineering teams to transition successful concepts into production. Represent TransUnion through publications, patents, and presentations while shaping long-term AI strategy.

Leads client-facing data science engagements for financial services customers, developing predictive risk management and business intelligence solutions across consumer lending. Performs descriptive, predictive, and prescriptive analysis using advanced statistical techniques and large-scale datasets. Programs data extraction and analysis in R, SAS, SQL, Hive, and Pig; presents recommendations to executives and customers; supports sales proposals and product adoption; mentors junior colleagues; and manages multiple initiatives with limited supervision.

Leads client-facing pre-sales engagements for credit analytics solutions. Responsibilities include conducting client meetings and workshops, creating presentations and proposals, translating analytical outputs into business value narratives, demonstrating ROI, supporting strategic sales opportunities, addressing objections, and coordinating post-sale communication with Product, Data Science, and Operations teams.

Lead technical product strategy and execution for TransUnion's GenAI platform. Define reusable platform capabilities (agentic AI, RAG, knowledge/context services, ontologies), translate business problems into architecture-aligned requirements, shape non-functional requirements, participate in technical tradeoffs, and partner with engineering, architecture, data science, and product teams to deliver enterprise AI platform capabilities.

The Lead Data Scientist will manage projects on credit risk and business intelligence analytics, leveraging statistical models to provide insights for financial services clients. Responsibilities include developing solutions, leading client engagements, and mentoring junior staff.

The role involves driving development in data science platforms, enhancing analytic tools, and leading data science initiatives. Candidate will support teams across regions and mentor juniors.

Provides senior-level data science and analytics consulting focused on credit risk, statistical modeling, econometric analysis, and machine learning. Develops, tests, documents, implements, and validates models; translates market and economic trends into product solutions; supports sales and client needs assessments; and presents analytical insights to management and customers. Coordinates cross-functional projects, ensures regulatory and privacy compliance, integrates solutions into client processes, and identifies business and revenue opportunities.

Leads complex analytics and AI consulting engagements from opportunity shaping through solution design, model development, validation, deployment readiness, and monitoring. Provides technical leadership across machine learning, generative AI, LLMs, RAG, MLOps, and responsible AI. Partners with clients and internal teams to deliver business value, communicates insights to technical and business stakeholders, contributes to innovation and governance, and mentors junior colleagues. Travel is required 10–20% of the time.

Principal-level senior individual contributor and strategic co-leader for Financial Services Consulting Data Science and Analytics. Drives strategy, operating model design, revenue and capacity planning, analytics consulting delivery, AI and machine learning solution development, product commercialization, data governance, platform stewardship, thought leadership, and talent development. Guides production-grade model pipelines, supports client engagements and sales, and ensures scalable, compliant analytics outcomes across a matrixed organization.

Design and enforce LLM guardrails and secure AI agent execution; implement cloud DLP and IAM across AWS/GCP; integrate security into CI/CD using Harness; secure artifacts in JFrog Artifactory; conduct threat modeling for LLMs, RAG pipelines, and agentic workflows; develop AI-driven security scanning.