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
As an ML Data Quality Lead at our company, you will thrive in a dynamic and innovative remote work environment that prioritises collaboration and professional growth. We offer competitive rates, a strong focus on employee development, and the opportunity to shape data quality strategies that directly impact business outcomes. Join us to be part of a culture that values excellence, creativity, and the pursuit of meaningful work in the rapidly evolving field of machine learning.
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
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at meetups. We all know that sometimes it’s not just what you know, but who you know that can help you land that dream job.
✨Tip Number 2
Prepare for those interviews by brushing up on your technical skills and understanding the latest trends in ML data quality. We recommend doing mock interviews with friends or using online platforms to get comfortable with the process.
✨Tip Number 3
Showcase your projects! If you've worked on any relevant ML data quality initiatives, make sure to highlight them during interviews. We love seeing real-world applications of your skills, so bring your A-game!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we’re always looking for passionate candidates who want to make an impact in the ML space.
We think you need these skills to ace ML Data Quality Lead in England
Some tips for your application 🫡
Tailor Your CV:Make sure your CV speaks directly to the role of ML Data Quality Lead. Highlight your experience with data quality initiatives and any relevant tools you've used, like SQL and Python. We want to see how your skills align with our needs!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about data quality in ML and how you can contribute to our team. Be sure to mention specific projects or achievements that showcase your expertise.
Showcase Relevant Experience:When detailing your experience, focus on your hands-on work with data validation frameworks and cloud services. We love seeing real-world examples of how you've tackled data quality challenges in production environments.
Apply Through Our Website:We encourage you to apply through our website for the best chance of getting noticed. It helps us keep track of applications and ensures you’re considered for the role. Plus, it’s super easy!
How to prepare for a job interview at Gravitas Recruitment Group (Global) Ltd
✨Know Your Data Quality Dimensions
Make sure you understand the key data quality dimensions like completeness, accuracy, and timeliness. Be ready to discuss how you've implemented these in past projects, as this will show your expertise and alignment with the role's requirements.
✨Showcase Your Technical Skills
Brush up on your SQL and Python skills before the interview. Prepare to share specific examples of how you've used data validation frameworks or orchestration tools in your previous roles. This will demonstrate your hands-on experience and technical proficiency.
✨Prepare for Stakeholder Management Questions
Expect questions about how you've influenced engineering roadmaps or communicated risks to stakeholders. Think of examples where you've successfully managed relationships and driven data quality initiatives, as this is crucial for the role.
✨Familiarise Yourself with MLOps and Governance
Since the role involves MLOps tooling and data governance, make sure you can discuss your familiarity with these concepts. Highlight any experience you have with regulatory expectations like GDPR, as this will show you're well-rounded and aware of industry standards.
