About the role
Data Quality Analyst (AI Model Data Analyst) Job Type: Full-time, Permanent Location: Portugal (1-2 days in office per week) Travel: Occasional international travel may be required. Salary: €16,000–€20,000 per annum Focal Systems is the industry leader in retail AI solutions. Our mission is to automate and optimize brick-and-mortar retail using deep-learning computer vision. Focal has been deployed at scale with the top retailers in the world. We are looking for smart, creative, and passionate people who love to learn, enjoy thinking critically, share our values, and want to build a great and enduring company. We have built the next-generation operating system for brick & mortar retail. This OS leverages shelf-mounted cameras that constantly measure the state of the retailers’ shelves and use that information to order quicker and smarter, planogram better, and schedule, direct, train, and manage their staff. We have proven this with major retailers around the world. Job Description The quality of our computer-vision models depends entirely on the quality of the data they learn from. We are seeking a dedicated Data Quality Analyst to clean, review, and curate the training data that powers our systems. In this role you will assume accountability for the accuracy and consistency of our datasets, directly improving how well our models detect products. Meticulous attention to detail, strong data-handling skills, and the ability to meet quality and speed Key Performance Indicators (KPIs) are essential. What You Will Be Doing Review, clean, and correct labelled image and product data used to train our computer-vision models. Identify and fix mislabelled, inconsistent, or low-quality data, and flag systematic errors in the labelling pipeline. Conduct routine quality-assurance checks to catch data issues before they reach model training. Onboard and scan products into the system, ensuring catalogue and reference data are accurate and complete. Compare model predictions against ground truth to surface edge cases and areas where the model underperforms. Work collaboratively with the machine-learning and operations teams to improve labelling guidelines and data standards. Meet or exceed speed and quality Key Performance Indicators (KPIs) consistently. Communicate effectively with supervisors and team members, providing updates on progress and raising any issues or challenges encountered.
