Company
Data Team
TechnicalProductManager(DataSearch&AIsquad)
Neural analysis suggests this role is
optimal for Senior candidates.
“Technical Product Manager (Data Search & AI squad). Skills: Data Search, AI, Semantic search, Agentic search. Own product strategy and roadmap. Lead productisation of semantic search”
Industry & Context.
Solving challenges; Tradeoff calls; Coverage vs precision; Hallucination tolerance
What They're Looking For.
Must Have
3+ years Product Manager experience, AI/ML/Search product experience, Working understanding of modern AI, Product thinking, Comfort making product tradeoffs in AI, Experience defining evaluation frameworks, Experience working with cross-functional teams, Comfortable operating in ambiguity
What You'll Do.
Own product strategy and roadmap
Lead productisation of semantic search
Lead productisation of agentic search
Shape AI stack at product level
Design eval sets and metrics
Use metrics to drive iteration
Decide when models are good enough
Drive customer discovery
Partner with Data Product squads
Partner with Platform squads
Work with stakeholders outside Product
How You'll Work.
Team & Collaboration
Cross-functional teams; Data Product squads; Platform squads; Sales; Customer Success; Engineering leadership
Process & Methodology
Roadmap, Prioritisation, Owning outcomes
Full Job Description
## Description We’re a team of 500+ professionals who develop cutting-edge proxy and web data scraping solutions for thousands of the world’s best known businesses, including Fortune 500 companies. What’s in store for you: You’ll be developing complex products with high coding standards, maintaining our own infrastructure, handling petabytes of data, and solving challenges on a daily basis. We got you covered with a team of strong professionals to support you, a well-built tech stack, and loads of ownership. ## Your day-to-day Own the product strategy and roadmap for Data Search & AI - what we build, why, and how it ladders up to the company's agentic search bet. Lead the productisation of semantic search and agentic search over our data: deciding what queries we support, what "good" answers look like, and how AI capabilities are exposed to customers. Shape the AI stack at a product level - embedding models, retrieval strategies, ranking, reranking, agent design - making the tradeoff calls between relevance, latency, cost, and quality. Own evaluation: design the eval sets and metrics that determine whether the search is actually good, and use them to drive iteration. Decide when models are good enough to ship and when they need more work - coverage vs precision tradeoffs, hallucination tolerance, when to fall back to deterministic approaches. Drive customer discovery on how customers actually want to query our data - what they ask, what frustrates them today, what an AI-native interface should let them do that a traditional API or UI cannot. Partner closely with the Data Product and Platform squads on what data is being indexed and what new datasets unlock new search capabilities. Work with stakeholders outside Product - Sales, Customer Success, Engineering leadership - to keep the squad connected to commercial and operational reality. ## To be successful you need to have At least 3 years of proven experience as a Product Manager or Technical Product Manager on a p
Applying for this Technical Product Manager (Data Search & AI squad) role?
Most applicants get filtered before a human reads their resume. See if yours makes the cut.
How to Apply on Lever
- Lever uses a streamlined one-page form — apply in under 5 minutes.
- LinkedIn import works well; review parsed data before submitting.
- The cover letter field is optional but visible to reviewers — use it to differentiate.
- Referral codes from employees can significantly boost visibility of your application.
ANONYMOUS · UNFILTERED
What do employees actually say about this company?
Real rants from real employees. Read before you apply.