Product Operations Analyst (L1)

CommerceIQ is building the AI platform that runs commerce for the world's largest brands. We are not selling AI demos. We are shipping AI agents for content, media, and sales into the workflows of the Fortune 100 every week.

Customers include Coca-Cola, Nestlé, Colgate-Palmolive, Mondelez, Samsung, and Kellogg's. Backed by SoftBank, Insight Partners, and Madrona. Headquartered in Mountain View with teams across the US, India, Canada, and the UK. Pre-IPO.

About the Role
We are looking for an entry level Product Operations Analyst to support and scale product data operations for our Digital Shelf Optimization platform.
This role is responsible for maintaining the quality, accuracy, and scalability of product operations across customer accounts. The day-to-day work is hands-on operational execution;product tagging, banner tagging, PIM matching, catalog maintenance and configuration changes across customer accounts. It demands accuracy, consistency and a high tolerance for detail. We are actively investing in AI-assisted and automated operations and the analysts who lean into that work are the ones who move up to L2 and beyond.

Key Responsibilities

• Execute day-to-day product operations: product tagging, banner tagging, PIM matching, catalog maintenance, and configuration updates across customer accounts, to defined quality standards and turnaround times.
• Validate product classifications, taxonomy mappings, and catalog configurations to maintain high data quality.
• Follow operational playbooks and SOPs consistently, and flag cases where the process is unclear, broken, or producing errors.
• Resolve routine operational issues, and escalate the ones that need cross-functional input with enough context to be actioned quickly.
• Learn the workflows well enough to identify repetitive, rules-based steps that are candidates for automation — and raise them.

Required Qualifications
• Graduate (bachelor's degree), with at least one internship in operations, data, analytics, or a related hands-on role.
• Strong attention to detail and a genuine bar for getting things right rather than just done.
• Comfort working with large datasets and structured, repetitive workflows without losing accuracy.
• Exposure to SQL, Python, or any scripting, even from coursework or personal projects.
• Clear written and verbal communication.
• Curiosity about how the work could be done faster or automatically and the drive to act on it.
• Hands-on experimentation with generative AI or automation tools.

How success is measured
• Accuracy and throughput on product operations activities.
• SLA adherence on change requests and operational tasks.
• Consistency and reliability of execution against SOPs.
• Automation opportunities surfaced and, over time, contributed to.