Lead Machine Learning Engineer

Description

The company builds Scores & Attributes for several international markets, and this role leads the technical work to turn analytical models and large data assets into reusable, governed, production-ready capabilities.

You will report to the Head of Analytics Product Development for EMEA and APAC. The role sits between machine learning, data engineering and platform engineering.

This is a hands-on leadership role. You will set architecture and engineering direction and also build complex solutions yourself, while mentoring engineers and data scientists and setting standards so teams can work independently.

Responsibilities

• Lead the design and growth of the capabilities used to develop, calculate, test, govern, deploy and run data attributes at scale.
• Build reusable frameworks, tooling and automation that move attributes from experiments and prototypes into governed production services.
• Make sure solutions are performant, scalable, reliable and easy to maintain.
• Define the end-to-end architecture and engineering patterns for the attribute lifecycle, from analytical development through governance and deployment to production.
• Design integration patterns so attribute services work with existing data and bureau platforms with minimal effort for consuming systems.
• Lead performance and scalability validation and resolve complex cross-platform integration problems.
• Assess new data sources and methods, measuring how much predictive and analytical value they add.
• Create and apply ways to show the value of the company data assets and turn them into new attributes and capabilities.
• Investigate approaches such as synthetic data for analytical experiments, product development and data offerings.
• Design reusable methods for onboarding new data assets and existing attribute libraries, covering ingestion, transformation and ETL pipelines.
• Set data-quality controls and engineering practices.
• Plan and carry out migrations of legacy attributes and data processes to strategic platforms without disrupting the business.
• Lead hands-on work on complex implementations and proofs of concept across several markets.
• Set reference architectures, reusable implementation patterns, engineering standards and development practices that teams can adopt.
• Run architecture and code reviews, troubleshoot hard technical issues, and mentor engineers and data scientists.
• Help local analytics and engineering teams adopt new capabilities and run them independently.

Requirements

• Strong software engineering foundations: object-oriented programming, data structures, algorithms, design patterns, testing and maintainable design.
• Hands-on Python experience with its data and analytics libraries, such as Pandas and NumPy.
• Experience building production-grade data or ML applications, not only prototypes or notebooks.
• Experience designing APIs and services that expose data or ML capabilities. FastAPI or similar frameworks are a plus.
• Experience with the software development lifecycle, Agile practices, code review and version control.
• Practical knowledge of applied machine learning and statistics, including model evaluation, feature engineering, overfitting, cross-validation and proper train, validation and test splits.
• Experience with large-scale data processing, such as Apache Spark, PySpark and Spark SQL.
• Experience with Databricks or a similar modern data and ML platform, including data processing, jobs and workflows, and the ML lifecycle.
• Understanding of modern data technologies such as Delta Lake, distributed processing and data-quality controls.
• Experience building reusable features or attributes for ML or analytics applications.
• Experience with the ML lifecycle, model and feature tracking, deployment or MLOps is a plus.
• Practical experience with at least one major cloud, Amazon Web Services or Azure, and the ability to apply that knowledge to other cloud environments.
• Understanding of core cloud concepts: compute, object storage, networking, identity and access management, security and managed data services.
• Experience with AWS services such as S3, EC2, Lambda, EKS, SageMaker, OpenSearch and managed Kafka, or equivalents.
• Experience with Azure services such as Blob Storage, Virtual Machines, networking, Microsoft Entra ID, Databricks and Azure Data Factory is a plus.
• Ability to design cloud solutions that balance performance, scalability, reliability, security and cost.
• Experience designing and working with CI/CD pipelines. Jenkins experience is a plus.
• Experience with Infrastructure as Code, such as Terraform.
• Hands-on knowledge of Docker, Kubernetes and container-based deployment.
• Comfortable working in Linux environments.
• Experience troubleshooting production systems and finding performance, scalability and reliability bottlenecks.
• Understanding of monitoring, logging and operations for production data and ML services.
• Ability to turn requirements into technical designs and weigh architectural trade-offs.
• Ability to design reusable solutions and integration patterns that scale across teams, markets and technology environments.
• Experience leading design and architecture reviews and setting engineering standards.
• Ability to review and improve code from engineers and data scientists for performance, maintainability and production readiness.
• Ability to give hands-on technical leadership while mentoring and enabling other engineers.
• Ability to explain complex technical ideas to both technical and non-technical audiences.
• Ability to work across engineering, analytics, product and geographically spread teams.
• Ability to compare options, make technical decisions and manage technical and delivery risk.
• Desirable: Apigee experience, SSO and identity integration experience such as Okta, migrating legacy analytical workloads to modern cloud platforms, building platforms, frameworks or tooling for data science and analytics teams, and experience in regulated, data-intensive settings such as financial services.

Conditions

Hybrid role combining remote and in-office work.

Flexible working hours and home office are available.

Equal opportunity employer; candidates who need accommodation can ask at any stage.

Benefits

• Career pathway with learning and development programs and unlimited access to online courses, learning materials and books.
• Friendly work environment, a strong team spirit and recreation time.
• Benefits package including life insurance, food vouchers, supplementary health insurance, dental cover, a monthly flex allowance, corporate discounts, marriage and childbirth or adoption allowances, a Multisport card, a Sharesave plan, an employee assistance programme and a birthday gift.
• 25 days of paid vacation, an extra day off for your birthday and 3 extra paid days for a social responsibility event.