Data Analyst – Tagging and Data Enrichment

About the role

M Science is seeking a detail‑oriented Data Analyst to structure and enrich large‑scale datasets through tagging and categorisation. You will turn raw and semi‑structured information into high‑quality, well‑organised data that powers downstream analytics, research, and client‑facing products.

Key responsibilities

• Develop, maintain, and improve tagging taxonomies and classification schemas for large datasets.
• Apply tags and labels to raw and semi‑structured data, ensuring consistency, accuracy, and completeness.
• Perform quality‑assurance reviews to identify misclassifications, gaps, and anomalies.
• Analyse tagging coverage and accuracy metrics, producing regular reports and recommendations.
• Collaborate with data engineering to integrate tagging logic into automated pipelines.
• Partner with research analysts and product managers to translate evolving tagging requirements into actionable specifications.
• Document tagging guidelines, rules, and decision frameworks for reproducibility and scalability.
• Identify opportunities to automate tagging processes using rule‑based or algorithmic approaches.

Required profile

• Strong attention to detail and passion for data quality.
• Ability to work cross‑functionally with engineering, product, and research teams.
• Experience with classification taxonomies or tagging schemas.

Required skills

• SQL for querying, validating, and analysing datasets.
• Python for data manipulation and analysis (e.g., pandas).