ML Data Quality Lead - Manchester

ML Data Quality Lead - Manchester

Fully remote working

Duration 2 Months

Day rate - Outside IR35 upto £650

A leading client in Manchester is hiring an ML Data Quality Lead to run a sprint-based program, with most technical tasks front-loaded. The role fills a key gap, overseeing data quality, validation, and model input integrity for a complex machine learning pipeline under tight deadlines.

Key duties include managing the end-to-end data and model validation process-from raw data ingestion through output auditing. Tasks cover adversarial test design, feature normalization, data quarantine, privacy-preserving ML, and ensuring model decisions are auditable.

Key skills and responsibilities,

• Practical experience in designing and running adversarial test suites for machine learning models in both production and near-production settings.
• Skilled in applied ML data engineering, including feature normalisation, building data validation pipelines, and implementing scalable quality rules.
• Knowledge of privacy-preserving machine learning techniques such as data minimisation, managing consent, and understanding differential privacy concepts.
• Experienced in defining ML output formats that can be audited, as well as setting up structured logging to track model decisions.
• Acquainted with data governance standards, including crafting retention policies, meeting data residency obligations, and designing tenancy models.
• Proven ability to deliver results in high-pressure program launches, as opposed to only working on standard project-based assignments.

Interested? Please submit your updated CV to Dean Sadler-Parkes at Harvey Nash for immediate consideration.

• Practical experience in designing and running adversarial test suites for machine learning models in both production and near-production settings.
• Skilled in applied ML data engineering, including feature normalisation, building data validation pipelines, and implementing scalable quality rules.
• Knowledge of privacy-preserving machine learning techniques such as data minimisation, managing consent, and understanding differential privacy concepts.
• Experienced in defining ML output formats that can be audited, as well as setting up structured logging to track model decisions.
• Acquainted with data governance standards, including crafting retention policies, meeting data residency obligations, and designing tenancy models.
• Proven ability to deliver results in high-pressure program launches, as opposed to only working on standard project-based assignments.