Product analyst in data quality

We are seeking four detail-oriented and analytical Product Analysts to support our Data & Risk product family on an extendable 12-month contract basis. This is a flexible, hands-on role spanning two closely connected areas of work: Product analysis, quality and validation for Data Enrichment products; and Data labeling and AI/model quality validation for Machine Learning capabilities.

• Support the Merchant Information product team in analysing product performance, quality signals, customer feedback, and operational data.
• Query, structure, and analyse data to identify patterns, recurring issues, inconsistencies, and opportunities for product improvement.
• Create clear visualisations, reports, and summaries that help stakeholders understand performance, quality, progress, and priority areas.
• Support product discovery and delivery by clarifying requirements, documenting expected behaviour, and translating feedback into structured findings.
• Assist with testing, validation, and quality assurance activities, including validating product behaviour during releases, migrations, and changes to data-processing logic.
• Investigate discrepancies between expected and actual product outputs and work with relevant teams to define and track resolution actions.
• Maintain lightweight documentation of findings, quality assessments, test outcomes, decisions, and follow-up actions.
• Label and classify transaction and merchant data used to train and improve machine learning and AI models.
• Review and quality assure labeled datasets to ensure accuracy, consistency, completeness, and adherence to defined guidelines.
• Validate AI-generated labels and model outputs, identifying misclassifications, edge cases, data-quality issues, and improvement opportunities.
• Analyse model and classification performance using appropriate measures, including precision, recall, accuracy, F1 score, and confusion matrices.
• Support validation activities for machine learning models and AI-assisted workflows, including models hosted on platforms such as Google Vertex AI.
• Work with Data Scientists and Machine Learning Engineers to improve labeling guidelines, training-data quality, validation processes, and model performance.
• Contribute to the evolution of internal tools, quality frameworks, validation processes, and operating practices as labeling workflows become increasingly automated.
• Improve the accuracy, consistency, and coverage of merchant and transaction categorisation data.
• Establish reliable quality checks for labeled data, AI-generated outputs, and product behaviour.
• Identify and document product, data, and model-quality issues with clear supporting evidence.
• Provide actionable analysis that helps product, engineering, and data-science teams prioritise improvements.
• Support confident releases and migrations through structured testing and validation.
• Strengthen documentation, reporting, and quality-management practices across Data Enrichment and machine learning models.

• 3+ years of relevant experience in a product analyst, data analyst, data quality, QA, product operations, data labeling, or similar role in a technology-driven environment.
• Strong analytical and problem-solving skills, with exceptional attention to detail and consistency.
• Comfortable working with data using tools such as SQL, spreadsheets, dashboards, or similar analytical tooling.
• Experience working with large datasets, data-quality processes, testing, validation, or business-rule assessment.
• Ability to investigate issues end-to-end, identify patterns and root causes, and communicate findings clearly.
• Understanding of core machine-learning concepts and workflows, including the importance of high-quality training data and model validation.
• Familiarity with classification problems and/or data annotation, labeling, or taxonomy-based workflows.
• Strong written and verbal English communication skills.
• Ability to collaborate effectively with both technical and non-technical stakeholders and to manage changing priorities.
• Highly detail-oriented, methodical, and quality-focused.
• Curious about how data, products, and AI systems work.
• Comfortable switching between operational execution and analytical problem-solving.
• Proactive in identifying issues and following them through to resolution.
• Able to work independently while collaborating closely with cross-functional teams.
• Comfortable operating in an evolving environment where priorities may shift between product analysis, testing, data validation, and labeling work.
• Будет плюсом: Experience analysing transaction data, merchant data, categorisation systems, or other financial-data classification problems; Experience with data labeling, annotation, model validation, or AI-assisted labeling workflows; Familiarity with model-evaluation concepts and metrics, including precision, recall, F1 score, accuracy, and confusion matrices; Experience supporting AI, machine-learning, MLOps, or data-platform environments; Familiarity with Google Vertex AI or comparable machine-learning platforms; Experience using SQL to investigate data issues and support product or quality analysis; Experience with test planning, regression testing, migration validation, or release-quality activities; Experience turning customer feedback or operational issues into structured analysis and product recommendations

• Условий в вакансии нет