Experian is a global data and technology company, powering opportunities for people and businesses around the world. We help to redefine lending practices, uncover and prevent fraud, simplify healthcare, create marketing solutions, and gain deeper insights into the automotive market, all using our unique combination of data, analytics and software. We also assist millions of people to realize their financial goals and help them save time and money.
We operate across a range of markets, from financial services to healthcare, automotive, agribusiness, insurance, and many more industry segments.
We invest in people and new advanced technologies to unlock the power of data. As a FTSE 100 Index company listed on the London Stock Exchange (EXPN), we have a team of 22,500 people across 32 countries. Our corporate headquarters are in Dublin, Ireland. Learn more at experianplc.com.
Job DescriptionAI-First Solutioning & Architecture (Core)
- Lead end-to-end architecture for AI/ML systems: data → features → training → evaluation → deployment → monitoring → retraining.
- Create and maintain reference architectures for:
- Batch scoring (e.g., nightly risk insights),
- Near-real-time decisioning (fraud, eligibility checks),
- Personalisation (ranking/next-best-action),
- Customer servicing intelligence (triage, routing, summarisation, agent assist).
- Drive build-vs-buy decisions for platforms and tools (ML platform, vector DB, model registry, feature store, orchestration, monitoring).
- Define non-functional requirements: latency, scale, resilience, privacy, explainability, auditability, cost, and operational ownership.
Applied ML Engineering (Hands-on)
- Build and productionise models across:
- Fraud/anomaly detection, entity resolution, risk scoring,
- Credit insights and predictive risk indicators,
- Propensity/CLV/churn models, uplift modelling, recommender/ranking systems,
- Customer servicing classification, intent detection, routing optimisation,
- (Where relevant) LLM-enabled workflows with evaluation + guardrails.
- Design rigorous evaluation strategies:
- Offline metrics (AUC/PR, calibration, cost-based thresholds),
- Backtesting and stability checks,
- Fairness/bias and subgroup performance,
- Online experimentation (A/B, multi-armed bandits where appropriate).
- Optimise for production constraints: inference latency, feature compute time, costs, reliability, and model robustness.
MLOps, Reliability & Observability (Staff expectation)
- Establish ML engineering standards: CI/CD for ML, reproducibility, model registry, lineage, approvals, and release strategies (canary/blue-green).
- Build monitoring for:
- Data quality + drift,
- Model performance decay and calibration drift,
- Pipeline health and SLA/SLO compliance,
- Business KPI impact and guardrail metrics.
- Define retraining triggers and human-in-the-loop workflows where required.
- Ensure secure-by-design practices: secrets management, encryption, IAM, audit logs, and incident runbooks for AI services.
Stakeholder Management & Business Partnership
- Partner with Product, Marketing, Risk, Compliance, Legal, Security, and Operations to:
- Frame problems, define success metrics, and quantify ROI,
- Navigate tradeoffs (accuracy vs latency vs cost vs explainability),
- Align on governance and operational ownership.
- Lead architecture reviews and communicate effectively to both technical and executive audiences.
- Translate consumer outcomes and business needs into measurable AI roadmaps.
Responsible AI, Governance & Privacy (Regulated environment)
- Build Responsible AI into the lifecycle: transparency, fairness, privacy, security, accountability.
- Produce governance artifacts: model cards, data documentation, validation reports, explainability evidence, and audit-ready change logs.
- Ensure UK/EU privacy-aware design practices: data minimisation, retention, lawful basis, access control, and privacy-by-design patterns.
Technical Leadership & Mentorship
- Mentor engineers/data scientists in production ML, architectural thinking, and engineering excellence.
- Establish patterns that scale across teams (templates, libraries, golden paths, reusable services).
- Raise quality across the org through design reviews, standards, and coaching.
What Success Looks Like
- AI initiatives deliver measurable outcomes in ECS UK domains:
- Fraud loss reduction, improved fraud detection recall/precision with low false positives,
- Better consumer credit insights and decision quality with calibrated outcomes,
- Increased marketing conversion/retention via personalisation and uplift,
- Reduced servicing handle time and improved first-contact resolution.
- Teams ship models faster and safer due to platform leverage, strong MLOps, and governance.
- Stakeholders trust and adopt your solutions due to clarity, auditability, and measurable impact.
Required Qualifications
- 12+ years in software/data/ML engineering with 3+ years leading architecture for production ML systems.
- Proven track record shipping multiple ML systems to production with measurable business impact.
- Strong ML foundations: leakage avoidance, calibration, imbalanced learning, evaluation rigor, drift management.
- Deep data expertise: SQL, data modeling, batch + streaming concepts, data quality and lineage.
- Cloud architecture experience (AWS/Azure/GCP) including security, networking, cost, reliability and observability.
- Strong stakeholder leadership: influence without authority, crisp communication, negotiation, and pragmatic delivery.
Preferred / Nice-to-have (Highly valued for ECS UK)
- Fintech experience: fraud, credit risk, identity, payments, AML/KYC, underwriting, collections.
- Real-time decisioning systems and feature stores (low latency constraints).
- Experience with marketing science: uplift, MMM/attribution concepts, propensity models, next-best-action/ranking.
- LLM application experience in regulated settings (RAG, evaluation harness, guardrails, red-teaming, prompt security).
- Familiarity with privacy, compliance, and audit expectations in UK/EU contexts
QualificationsQualifications
Typical Tech Stack (Representative; not mandatory)
- Languages: Python, SQL (strong); optional: Java/Scala
- ML: scikit-learn, XGBoost/LightGBM, PyTorch/TensorFlow
- Data: Spark/Databricks, Airflow/Dagster/Prefect, dbt (optional)
- Serving: FastAPI, Docker, Kubernetes, model serving frameworks (Seldon/KServe/Triton)
- Streaming: Kafka/Event Hubs/Kinesis
- MLOps: MLflow/W&B, feature store (Feast/Tecton), model monitoring tools
- Observability: OpenTelemetry, Prometheus/Grafana, ELK/Datadog
- Governance: access control, encryption, audit logging, documentation workflows
Example Initiatives You Might Lead
- Fraud detection + entity resolution platform with streaming features and low-latency inference
- Consumer credit insight models with explainability and audit-ready governance
- Personalisation engine (ranking/next-best-action) with experimentation + uplift measurement
- Customer servicing intelligence: triage + routing + agent assist with robust evaluation and guardrails
- Standardised ML deployment “golden path” improving time-to-production and reliability across ECS UK
Interview Loop (Suggested)
1) ML System Design (end-to-end + tradeoffs)
2) Applied ML Deep Dive (evaluation, drift, calibration, bias/fairness)
3) Coding/Data Exercise (production-quality Python/SQL)
4) Stakeholder/Business Case (ROI, prioritisation, governance)
5) Leadership & Mentoring (influence, standards, decision-making)
Additional InformationOur uniqueness is that we celebrate yours. Experian's culture and people are important differentiators. We take our people agenda very seriously and focus on what matters; DEI, work/life balance, development, authenticity, collaboration, wellness, reward & recognition, volunteering... the list goes on. Experian's people first approach is award-winning; World's Best Workplaces™ 2024 (Fortune Top 25), Great Place To Work™ in 24 countries, and Glassdoor Best Places to Work 2024 to name a few. Check out Experian Life on social or our Careers Site to understand why.
Experian is proud to be an Equal Opportunity and Affirmative Action employer. Innovation is an important part of Experian's DNA and practices, and our diverse workforce drives our success. Everyone can succeed at Experian and bring their whole self to work, irrespective of their gender, ethnicity, religion, colour, sexuality, physical ability or age. If you have a disability or special need that requires accommodation, please let us know at the earliest opportunity.
Experian Careers - Creating a better tomorrow together
Find out what its like to work for Experian by clicking here
What We Do
Experian unlocks the power of data to create opportunities for consumers, businesses and society. During life’s big moments – from buying a home or car, to sending a child to college, to growing a business exponentially by connecting it with new customers – we empower consumers and our clients to manage data with confidence so they can maximize every opportunity. We gather, analyse and process data in ways others can’t. We help individuals take financial control and access financial services, businesses make smarter decision and thrive, lenders lend more responsibly, and organizations prevent identity fraud and crime. For more than 125 years, we’ve helped consumers and clients prosper, and economies and communities flourish – and we’re not done. Our 20,600 people in 43 countries believe the possibilities for you, and our world, are growing. We’re investing in new technologies, talented people and innovation so we can help create a better tomorrow. About Experian: Bringing data to life requires creativity, passion, flexibility and expertise. We want you to share in our success. That's why we offer rewards that recognise great performance. Working in a culture of collaboration, achievement and respect we will give you the support and encouragement you need to develop your skills and talents and progress your career. Everyday our people bring enthusiasm, innovation and inspiration to work and if this sounds like you connect with us at Experian.
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