ML Data Quality Lead

ML Data Quality Lead
Location: UK Remote,
Rate:  Up to £630 per day DOE

Overview
We are seeking an ML Data Quality Lead to own and improve end-to-end data quality across machine learning and analytics products. You will define data quality strategy, standards, controls and monitoring to ensure trusted datasets for model training, evaluation and production performance, working closely with Data Engineering, ML Engineering, Analytics, Governance and Product.

Key Responsibilities

• Lead the data quality roadmap for ML use cases, aligning quality objectives to business outcomes and model risk.
• Define data quality dimensions, rules and thresholds (completeness, accuracy, timeliness, consistency, validity, uniqueness) and implement automated controls.
• Design and maintain data validation, anomaly detection and drift monitoring for features, labels and reference data.
• Establish data lineage, documentation and metadata standards to support auditability and reproducibility.
• Build dashboards and alerting for data quality KPIs; run incident triage and root-cause analysis with clear remediation plans.
• Partner with ML teams to ensure training/serving consistency and robust dataset curation and versioning.
• Embed quality checks into CI/CD data pipelines and model pipelines; champion testing practices (unit, integration, regression).
• Support governance, privacy and security requirements, including access controls and data handling standards.

Required Skills & Experience

• Proven experience leading data quality initiatives for ML/AI or advanced analytics in a production environment.
• Strong SQL and Python skills; experience with data validation frameworks (e.g., Great Expectations, Deequ) and orchestration tools (e.g., Airflow, Prefect).
• Experience with data platforms/warehouses and lakehouse patterns; proficiency with cloud services (AWS, Azure or GCP).
• Working knowledge of ML concepts, feature engineering, label quality, bias, and monitoring for drift and data integrity.
• Hands-on experience with observability, logging and dashboarding (e.g., Grafana, Datadog, Prometheus, Kibana).
• Familiarity with data governance, data protection, and regulatory expectations (e.g., GDPR) and strong documentation discipline.
• Excellent stakeholder management, ability to influence engineering roadmaps, and clear communication of risk and priorities.

Desirable

• Experience with MLOps tooling and model monitoring platforms; data catalogue/lineage tools.
• Exposure to domain-specific quality frameworks and operating models for cross-functional teams.