Vilnius
Data Team
Design and ship multi-source datasets by combining sources in ways customers can't easily replicate - figuring out what new value emerges when you fuse the right inputs.
Lead the productisation of ML and AI capabilities into the data itself.
Make the hard tradeoff calls on ML-powered fields - coverage vs. precision, latency vs. cost, when a model is good enough to ship and when it needs another iteration.
Define what "good" data quality means from the customer's perspective - accuracy, completeness, freshness as commitments we make to customers - and own the business-level rules that determine whether the data is correct.
Drive customer discovery: spend time with existing and prospective customers, understand what data problems they're trying to solve, where current datasets fall short, and what would unlock new revenue if it existed.
Work with stakeholders outside Product - sales, customer Success, marketing - to keep the roadmap connected to commercial reality.
Own backlog, sprint planning, and user stories - but as the natural output of strategy, not the job itself.
At least 5 years of proven experience as a Product Manager, Technical Product Manager, or Senior Product Owner, ideally on a data, API, or platform product.
Strong product thinking: discovery, customer research, prioritisation, owning outcomes - not just managing a backlog.
Technical fluency: comfortable reasoning about data pipelines, schemas, transformations, and inferred fields well enough to make tradeoff decisions and have credible conversations with engineers.
Experience designing or shaping datasets as a product — deciding what fields exist, what they mean, how they evolve.
Experience working with cross-functional teams and stakeholders outside product (Sales, Customer Success, Marketing).
Comfortable operating in ambiguity — this is a new role and the person needs to help shape it, not just inherit a defined role.
Strong written and verbal communication in English.
Experience working in an agile environment.
Experience with B2B data, SaaS, or API products.
Hands-on familiarity with how data moves through a system - extraction, transformation, storage, enrichment, delivery.
Experience working on a product where data quality is a customer commitment, not just an internal metric.
Experience splitting or scaling product squads, or being part of a newly formed team.
Familiarity with how LLMs, embeddings, or semantic search consume structured data - useful for working with the CASE squad.
Živilė Repšytė
Recruiter
Interested?
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