operations
Posted 2 hours agoProduct Operations Analyst (L1)
at CommerceIQ
Bengaluru, IndiaOn-site
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.
- Resolve routine operational issues, and escalate the ones that need cross-functional input with enough context to be actioned quickly.
Requirements
- 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.
- 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
- Graduate (bachelor's degree), with at least one internship in operations, data, analytics, or a related hands-on role.
- Exposure to SQL, Python, or any scripting, even from coursework or personal projects.
- Hands-on experimentation with generative AI or automation tools.
Additional details
- 2,200+ Customers 10 of Top 12
- 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
- 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.
- Follow operational playbooks and SOPs consistently, and flag cases where the process is unclear, broken, or producing errors.
- 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.
- Curiosity about how the work could be done faster or automatically and the drive to act on it.