At a Glance
- Tasks: Lead data quality initiatives for ML products and ensure trusted datasets.
- Company: Innovative tech company focused on machine learning and analytics.
- Benefits: Competitive daily rate, remote work flexibility, and opportunities for professional growth.
- Other info: Collaborative environment with excellent career advancement potential.
- Why this job: Make a real impact on data quality in cutting-edge ML projects.
- Qualifications: Experience in data quality for ML, strong SQL and Python skills required.
The predicted salary is between 50400 - 63000 £ per year.
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.
ML Data Quality Lead in England employer: Gravitas Recruitment Group (Global) Ltd
Join a forward-thinking organisation in Reading that is committed to digital transformation and innovation. As the Head of Cloud Infrastructure, you will benefit from a hybrid working model, a culture that values meaningful change, and ample opportunities for professional growth within a supportive team environment. With a focus on modernisation and a strong commitment to employee development, this role offers a unique chance to shape the future of cloud infrastructure while enjoying the stability of a well-established company.
Contact Details:
Gravitas Recruitment Group (Global) Ltd Recruitment Team
StudySmarter Expert Advice🤫
We think this is how you could land ML Data Quality Lead in England
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We think you need these skills to ace ML Data Quality Lead in England
Some tips for your application 🫡
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How to prepare for a job interview at Gravitas Recruitment Group (Global) Ltd
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