Product Analyst – Data & Risk (Location:Sweden)

About the Role 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:

1. Product analysis, quality and validation for Data Enrichment products; and
2. Data labeling and AI/model quality validation for Machine Learning capabilities.

The Product Analysts will move between these areas as business priorities require. They will help ensure that merchant and transaction data is accurately classified, product outputs meet expected quality standards, customer and operational feedback is translated into actionable insight, and product teams can make confident, data-informed decisions. This role is well suited to someone who enjoys structured problem-solving, analysing data and system behaviour, identifying quality issues, and working closely with product, engineering, data science, and operations stakeholders. Key Responsibilities Product Analysis and Quality Support

• 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.

Data Labeling and AI Quality Validation

• 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.

Required Skills and Experience

• 1–3 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.