Data Specialist, Recognition Services

Data Specialist, Recognition Services

Recognition Services is responsible for the digitization of the images received through various platforms - Mobile App, API, Installed Cameras, you name it. The higher image recognition accuracy and product database completion, the bigger success for our team. We aim to deliver high data quality both to our clients and our Computer Vision developers to get the perfect match between business and technological needs.

The Recognition Services Data Specialist plays a key role in ensuring project performance, recognition accuracy, and operational efficiency within the Recognition Services team.

This role blends data analysis, project management, and cross-functional collaboration, ensuring that recognition processes meet quality and delivery standards.

The position is ideal for professionals who enjoy working with data while coordinating multiple stakeholders in a dynamic, technology-driven environment.

Responsibilities

Data & Performance Analysis

• Monitor recognition accuracy, inflow, and other performance metrics across projects.
• Analyze large datasets to identify patterns, anomalies, and improvement opportunities.
• Create and maintain performance reports and dashboards to support project decisions.
• Partner with global teams to standardize accuracy tracking and reporting processes.

Project Management

• Manage day-to-day recognition operations across multiple projects and clients.
• Act as the main liaison between operational teams, technical experts, and delivery leads.
• Ensure recognition tasks are executed on time and aligned with project requirements.
• Identify and communicate process bottlenecks, proposing corrective or preventive actions.

Database & Quality Control

• Validate and update product and image databases to ensure consistency and accuracy.
• Support system testing and configuration for recognition models and workflows.
• Perform data quality checks and ensure compliance with internal accuracy benchmarks.

Continuous Improvement

• Contribute to the optimization of recognition processes through insights and feedback.
• Collaborate with automation, data, and delivery teams to scale efficiency initiatives.
• Support knowledge sharing and best practices across global Recognition Services teams.

What We Offer

• Working on state-of-the-art technology with an international team
• Wide range of benefits: healthcare, performance bonus, home office allowance
• Hybrid working model
• Career path internally
• Learning & development opportunities

Trax is committed to a diverse, inclusive, and equitable workplace where all team members, whatever their gender, race, ethnicity, national origin, age, sexual orientation or identity, education, or disability, feels valued and respected. We are committed to a nondiscriminatory approach and maintaining an inclusive environment with equitable treatment for all.

Minimum Qualifications

• Bachelor’s degree in Data Analytics, Business Administration, Engineering, or a related field.
• Minimum 2 years of experience in data-driven or project coordination roles.
• Proficiency in Microsoft Excel (Intermediate–Advanced) for reporting, data manipulation, and trend analysis.
• Working knowledge of SQL (Intermediate) for querying and validating performance datasets.
• Strong analytical, problem-solving, and organizational skills.
• Excellent communication skills in English (required).
• Ability to manage multiple priorities and work independently in a fast-paced environment.

Preferred Qualifications

• Experience working in global or cross-functional environments.
• Basic knowledge of Python for data cleaning and automation tasks.
• Experience in recognition systems, retail analytics, or similar data-intensive environments.
• Proficiency in Spanish for regional collaboration.

Success in this Role

• Maintain consistent recognition accuracy levels and contribute to their continuous improvement over time.
• Timely delivery of analytical insights and reports.
• Smooth coordination between technical and operational teams.
• Positive impact on process efficiency and data quality improvements.