Opptra - Product Lead

- Opptra is a digital-first omnichannel distributor helping global brands enter and scale across Asia's fastest-growing consumer markets - India, Southeast Asia, and the GCC. Founded by
- Binny Bansal (co-founder of Flipkart), Opptra operates on a stock-and-sell model, purchasing inventory upfront and running end-to-end operations from market entry strategy through to post-purchase experience. With 28 global brands, 18 warehouses, and presence across 15+ marketplaces, Opptra is building the operating system for brand expansion in Asia.
Role Summary:
- Opptra is building the intelligence layer that helps global brands price, buy, and compete across Asia's most complex marketplace ecosystems - Amazon, Noon, Flipkart, Shopee, and Lazada.
- The Product Lead will own six AI-driven charters spanning pricing, inventory, market intelligence, performance marketing, data warehousing, and cataloguing, leading a team of 5-6 AI Product Builders while co-developing strategy directly with the Founder.
- The role demands genuine marketplace platform depth and hands-on AI product experience - the right candidate has shipped AI-core features to real users, understands model tradeoffs, and can build evaluation frameworks from scratch.
- This is a founding product leadership position suited to someone who operates effectively in high-ambiguity environments, without a mature org or a finished roadmap to lean on. Opptra is a small, fast, opinionated team - and this role is for someone who sees that as the point, not the caveat.
What you will build?
1. Pricing Intelligence:
- Dynamic pricing recommendations across Amazon, Noon, Flipkart, Shopee, and Lazada - Buy Box optimisation, competitive price benchmarking, and margin-aware repricing engines.
2. Inventory Intelligence:
- Demand forecasting, safety stock logic, and replenishment triggers across FBA, FBF, and seller-fulfilled nodes - accounting for event-driven demand spikes and multi-geography warehouse positioning.
3. Market Intelligence:
- Share of shelf, competitor tracking, category demand signals, grey market detection, and MAP compliance monitoring across 15+ marketplaces.
4. Performance Marketing:
- AI-driven ad budget allocation, ROAS optimisation, keyword strategy, and campaign performance intelligence across sponsored placements on marketplace platforms.
5. Data Warehousing:
- BigQuery-backed data infrastructure that powers all six charters - ingestion pipelines, crawlers, data freshness SLAs, and the cross-marketplace data models that make the flywheel possible.
6. Catalogue Intelligence:
- Automated listing migration, content quality scoring, attribute harmonisation across marketplace taxonomies, and AI-assisted catalogue optimisation at scale.
WHAT YOU WILL DO?

1) Own the AI product roadmap across all six charters - from strategy to specification to production-grade delivery
2) Build and maintain evaluation frameworks that measure model performance in production, not just in development
3) Collaborate directly with the Founder to set product priorities, make architecture tradeoffs, and define what gets cut
4) Lead 5-6 AI Product Builders - defining how the team operates, reviews its work, and ships without being managed
5) Write AI product specifications that include model behaviour bounds, eval criteria, fallback logic, and override architecture - not just user stories
6) Ensure commercial teams across India, SEA, and GCC adopt and rely on AI products - not just tolerate them
7) Build the data flywheel architecture where every brand is onboarded, every pricing decision is logged, and every demand signal is captured makes the next recommendation sharper
8) Work with FDEs and data engineers to close the product-to-engineering gap - through clean handoffs, shared definitions of done, and post-launch performance ownership
WHAT WE NEED: Non-negotiable
AI Product Depth:
- You have shipped AI-core features to real users - not prototypes, not wrappers. You've built evaluation frameworks from scratch, owned model failures in production, and understand the tradeoffs between fine-tuning, RAG, and prompt-based approaches.
Marketplace Fluency:
- You have worked with Amazon, Flipkart, Shopee, Lazada, or Noon close enough to the platform mechanics that you can speak to Buy Box logic, listing quality scoring, demand signal structure, and multi-seller attribution without being prompted.
Founding Mentality:
- You've operated in a small, high-conviction team where the roadmap was yours to define and the Founder was your primary stakeholder. You don't need org support to move fast or make hard calls.
Commercial Instinct:
- You understand that pricing intelligence, inventory decisions, and catalogue quality have direct margin implications. You can connect AI output quality to commercial outcomes - and you've tracked that connection in production.
Team-building at Scale:
You've led small technical teams and have a real model for building toward autonomy - not just delegating tasks, but developing people who can ship without being managed.
Strong preferences:
1) Prior experience at an e-commerce platform, marketplace intelligence company, or retail data product business.
2) Hands-on experience with BigQuery, data pipeline architecture, or crawling/ingestion systems - not necessarily as an engineer, but fluent enough to write meaningful specs and spot architectural risks
3) Experience building products for multi-geography, multi-marketplace environments where category dynamics, pricing structures, and taxonomy schemas differ materially by platform and market
4) Track record of preventing demo factory culture - systems and team practices that move prototypes to production, not just polish them.
WHAT THIS IS NOT?
1) This role is explicit about what it isn't, because the wrong fit is as consequential as no fit.
This is not a role for someone who needs an established team, a finished roadmap, or a large org to operate confidently
2) This is not a traditional PM role - you will be expected to understand model tradeoffs, write eval criteria, and own AI product failures personally
3) This is not a role where commercial teams come to you with requirements - you will need to generate insights they haven't asked for yet
4) This is not a role where 'shipped to staging' counts as shipped - production performance, commercial adoption, and measurable impact are the only metrics that matter