Machine Learning Scientist

Our Experian Software Solution's Analytics Services Team supports advanced analytic and generative AI products for decisioning, analytics, fraud, and identity globally.

As a Machine Learning Scientist, you will design, build, and deploy cutting‑edge machine learning and generative AI solutions at scale. You will combine deep data science expertise with ML engineering and data engineering capabilities to deliver production‑ready models, prototypes, and platform capabilities. You will also partner with cross‑functional teams to support new global product launches, client implementations, and early client adoption. You will report to the Director of Data Science and Gen AI.

What you'll do:

• You will partner with data scientists and then expand to cover packaging and productization of additional analytics solutions.
• You would deliver the last mile of those innovations, ensuring that production code is executed within the Ascend platform and software that Experian delivers to clients.
• Collaborate with Engineering, Research, and Data Science teams in the design and implementation of Machine Learning, Dashboarding, Ad Hoc Analysis and AI applications in a cloud-native big data (AWS) computing platform.
• Partner with Leaders, Analytic Consultants, Engineers, Account Executives, Product Managers, and external partners to bring new solutions to market that provide impact to Experian's broad client base.
• Craft advanced machine learning analytical solutions and prototypes to extract insights from diverse structured and unstructured data sources. Articulate model processes and outcomes, documenting and presenting findings and performance metrics, and translating complex findings into relevant insights.
• Use Gen AI and model development tools to develop new models and to prototype and deploy new generative capabilities, including prompt engineering, fine‑tuning, RAG, and model evaluation frameworks.
• Develop production-quality code following software engineering best practices, including modular design, version control, code reviews, and automated testing, ensuring reliability and reproducibility.
• Communicate model methodologies, assumptions, performance, and trade-offs to both technical and non‑technical stakeholders. Deliver clear documentation and translate complex concepts into insights.
• Design and implement advanced algorithms to solve complex challenges, exploring methods across supervised, unsupervised, deep learning, graph analytics, anomaly detection, and reinforcement learning.
• Contribute to feature and platform evolution by gathering feedback from clients and our teams, helping prioritize enhancements across Experian's ML and AI ecosystem.