Data Quality Analyst (AI Model Data Analyst)

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. Requirements:

• Strong attention to detail and a genuine care for data accuracy and consistency.

• Comfort working with large volumes of data, spreadsheets, and data-review or annotation tools

• Strong analytical and problem-solving skills, with the ability to spot patterns and root-cause data errors.

• An interest in or exposure to machine learning, computer vision, or AI data pipelines is advantageous but not essential.

• Time Management: ability to work well under pressure, meet deadlines, and ensure timely completion of assigned tasks.

• Communication Skills: effective communication to liaise with supervisors, team members, and the ML team, providing updates and raising issues.

• Ability to work independently and stay focused on repetitive, detail-heavy tasks for extended periods. Why Focal Systems

• Strong Values and Mission — We are a tightly-knit team with an ambitious mission and a strong set of core values, which define our approach to business and have successfully guided us since inception.

• Exceptional Team — We are a team of hard-working, fun-loving professionals from some of the most eminent universities, research labs, and tech companies of our time. We pride ourselves on recruiting exceptional individuals to help us redefine the state-of-the-art.

• Outstanding Partners — We work with 10+ of the largest retailers in the world and have a world-class roster of investors, advisors and partners to support & advise us in our endeavors. What we offer We care deeply about the health, happiness, and wellbeing of all of our employees. We offer:

• Paid Time Off

• Pension

• Sick Leave