Product Analyst - Data & Risk

Infinity Quest Limited; Stockholms län; On-site; SEK 550,000 - 700,000; Full time

Job summary

Infinity Quest Limited is seeking a Product Analyst to support the Merchant Information and Data Labeling and AI Quality Validation workstreams. You will analyse performance, quality signals, and customer feedback, and translate findings into actionable insights for product teams.

The role involves data querying, creating visualisations, supporting discovery, and participating in testing and validation across ML models and data workflows.

Qualifications

• 1–3 years of relevant experience in product analytics, data quality, QA, or related roles.
• Strong analytical and problem-solving skills with high attention to detail.
• Experience with SQL, spreadsheets, dashboards, or similar analytical tools.
• Experience handling large datasets and data-quality processes.
• Ability to investigate issues end-to-end and communicate findings clearly.
• Knowledge of ML concepts and model validation is a plus.

Responsibilities

• Analyse product performance, quality signals, customer feedback, and operational data.
• Query, structure, and analyse data to identify patterns and opportunities for product improvement.
• Create clear visualisations, reports, and summaries for stakeholders.
• Support product discovery by clarifying requirements and translating feedback into findings.
• Assist with testing, validation, and QA during releases and migrations.

Skills

SQL

Data analysis

Quality assurance

Data visualization

English communication

Tools

Google Vertex AI

Job description

• 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.

Preferred Skills and Experience

• 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.

What You Will Deliver

During the engagement, Product Analysts will help the team to:

• 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.

Personal Attributes

We are looking for people who are:

• 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.