Lead Machine Learning Engineer

Company Description

Experian is a global data and technology company, powering opportunities for people and businesses around the world. We operate across a range of markets, from financial services to healthcare, automotive, agribusiness, insurance, and many more. Experian invests in people and new advanced technologies to unlock the power of data. We have an amazing team of 25,200 people in 32 countries.

Job Description

We are looking for a Lead ML Engineer to provide technical leadership for the development and industrialisation of Scores & Attributes across multiple international markets. You will report to the Head of Analytics Product Development - EMEA and APAC. You will work at the intersection of machine learning, data engineering and platform engineering, helping transform analytical innovation and large-scale data assets into reusable, governed and production-ready capabilities.

You will lead the technical journey from data exploration and attribute development through testing, governance, deployment and production execution. An important part of the role will design reusable architectures and integration patterns that allow these capabilities to grow across markets and existing technology environments.

This is a hands-on technical leadership role: you will combine architecture and engineering leadership with direct contribution to complex implementations. You will mentor engineers and data scientists and establishing standards that help teams to operate independently.

What you will do:

ML Engineering & Attribute Industrialisation

Lead the design and evolution of capabilities for developing, calculating, testing, governing, deploying and operating data attributes at scale. Build reusable frameworks, tooling and automation that allow attributes to move from experimentation and prototyping into governed, production-ready services. Ensure solutions meet requirements for performance, scalability, reliability and maintainability.

Technical Architecture & Platform Integration

Shape the end-to-end technical architecture and engineering patterns supporting the attribute lifecycle - from development in analytical environments through governance and deployment to production execution. Design integration patterns that allow attribute services to work with existing data and bureau platforms while minimising implementation effort for consuming systems. Lead performance and scalability validation and resolve complex cross-platform integration challenges.

Data & Attribute Innovation

Evaluate new data sources and methodologies, quantifying their incremental predictive and analytical value. Develop and implement approaches for demonstrating the value of Experian data assets and translating them into new attributes and capabilities. Explore technical approaches such as synthetic data to support analytical experimentation, product development and data propositions.

Data Engineering & Asset Onboarding

Design reusable approaches for onboarding new data assets and existing attribute libraries, including data ingestion, transformation and ETL pipelines. Establish data-quality controls and engineering practices. Develop approaches for migrating legacy attributes and data processes onto strategic platforms while maintaining business continuity.

Technical Leadership & Market Enablement

Provide hands-on technical leadership for complex implementations and proof-of-concepts across multiple markets. Establish reference architectures, reusable implementation patterns, engineering standards and development practices that can be adopted across teams. Conduct architecture and code reviews, troubleshoot complex technical issues, mentor engineers and data scientists, and help local Analytics and Engineering teams to adopt new capabilities and operate them independently.

Qualifications

What we're looking for:

Software & ML Engineering

• Software engineering fundamentals, including object-oriented programming, data structures, algorithms, design patterns, testing and maintainable software design.
• Hands-on experience with Python and its data/analytical ecosystem, such as Pandas and NumPy.
• Experience developing production-grade data or ML applications, rather than only analytical prototypes or notebooks.
• Experience designing APIs and services for exposing data or ML capabilities; experience with FastAPI or similar frameworks is desirable.
• Experience with software development lifecycle, Agile development practices, code review and version control.

Machine Learning & Data Processing

• Experience with applied machine learning and statistical concepts, including model evaluation, feature engineering, overfitting, cross-validation and appropriate train/validation/test methodologies.
• Experience working with large-scale data processing technologies, like Apache Spark / PySpark and Spark SQL.
• Experience with Databricks or a comparable modern data/ML platform, including data processing, jobs/workflows and ML lifecycle capabilities.
• Understanding of modern data technologies such as Delta Lake, distributed data processing and data-quality controls.
• Experience developing reusable features/attributes for ML or analytical applications.
• Experience with ML lifecycle, model/feature tracking, deployment or MLOps practices is desirable.

Cloud & Platform Engineering

• Practical experience with at least one major cloud platform - Amazon Web Services or Azure, and the ability to apply that knowledge across cloud environments.
• Understanding of core cloud concepts including compute, object storage, networking, identity and access management, security and managed data services.
• Experience with relevant Amazon Web Services services such as S3, EC2, Lambda, EKS, SageMaker, OpenSearch and managed Kafka services, or equivalents.
• Experience with Azure services such as Blob Storage, Virtual Machines, networking, Microsoft Entra ID, Databricks and Azure Data Factory is desirable.
• You can design cloud solutions for performance, scalability, reliability, security and cost efficiency.

DevOps & Production Engineering

• Experience designing and working with CI/CD pipelines; Jenkins experience is desirable.
• Experience with Infrastructure as Code, like Terraform.
• Hands-on knowledge of Docker and Kubernetes and container-based deployment patterns.
• Comfortable working in Linux environments.
• Experience troubleshooting production systems and identifying performance, scalability and reliability bottlenecks.
• Understanding of monitoring, logging and operational practices for production data/ML services.

Architecture & Technical Leadership

• You can translate requirements into technical designs and evaluate architectural trade-offs.
• You can design reusable solutions and integration patterns that can scale across multiple teams, markets and technology environments.
• Experience leading technical design and architecture reviews and establishing engineering standards.
• You can review and improve code produced by engineers and data scientists, including performance, maintainability and production readiness.
• Provide hands-on technical leadership while mentoring and enabling other engineers.

Communication & Collaboration

• Communicate complex technical concepts to both technical and non-technical audiences.
• Partner and work across engineering, analytics, product and geographically distributed teams.
• Evaluate alternatives and make technical decisions.
• Manage technical and delivery risks.

Desirable

• Experience with API management platforms like Apigee.
• Experience implementing SSO/identity integrations, such as Okta.
• Experience migrating legacy analytical/data workloads onto modern cloud platforms.
• Experience developing platforms, frameworks or tooling used by Data Science/Analytics teams.
• Experience in regulated, data-intensive environments such as financial services is advantageous.

Additional Information

What you will get:

• Personal Development - career pathway for professional growth supported by learning and development programs and unlimited access to online educational training courses, learning materials and books.
• Work environment - excellent work conditions with friendly environment, recognized team spirit, and fun and quality recreation time.
• Social benefit package including life insurance, food vouchers, additional health insurance, dental coverage, monthly flex allowance, corporate discounts, marriage and childbirth / adoption allowance, Multisport card, Sharesave plan, Employee assistance program, а birthday gift and many other benefits!
• Work-life balance - 25 days paid vacation, 1 additional day off for your birthday and extra 3 paid days for participation in Social responsibility event.
• Opportunity for Flexible working hours and Home Office.

   

  Our uniqueness is that we celebrate yours. Experian's people first, inclusive and purpose-driven culture is multi award-winning; World's Best Workplaces™ 2025 (Fortune Global Top 25), Great Place To Work™ in 26 countries to name a few. Check out Experian Life on social or explore our Careers Site to understand why. Experian is also proud to be an Equal Opportunity and Affirmative Action employer. If you have a disability or special need that requires accommodation, please let us know at the earliest opportunity.

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