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Lemrock builds the infrastructure of agentic commerce.
Tomorrow, consumers won't just buy on e-commerce websites. They will discover, compare and purchase directly inside conversational AI interfaces like ChatGPT, Perplexity, and a growing long tail of specialized AI assistants.
For brands, this is a major shift: if they are not present in these environments, they become invisible.
Our mission: enable any brand to exist, sell and distribute in this new channel.
We raised a €7M seed round (the first in Europe on this market), with Jean-Baptiste Rudelle, founder of Criteo, on our board, and the Bpifrance DeepTech label.
Today, Lemrock is already:
- 100M+ conversations per month
- 150+ brand clients
- a $100B market structuring at high speed
We are accelerating. The product works, clients renew, the market is taking shape. Every day, shoppers talk to AI agents before they buy, and performance has been at the heart of how we build the product from day one. We now want to go further and faster: understand precisely what makes our campaigns and recommendations perform, and apply it to the product continuously.
As our freelance Analytics Engineer, full-time, you will dig into our data to find what drives performance, then turn your findings into changes in the product. You will work directly with the founders and the product and engineering teams.
Understand what drives performance
- Explore every data source we have: conversations, web and behavioural events, campaign performance, product and advertiser metadata
- Find what drives performance and size the impact of each lever
- Measure every change with A/B tests, and feed the results back into the next analysis
Turn findings into changes
- Apply what you find to the product yourself, with Claude Code: which products we prioritise, who we show them to, how we present them
- Write and maintain the Claude Code skills that make these updates repeatable, reviewed by the engineering team
Build data that lasts
- Model our data in BigQuery so every analysis can be rerun, not rebuilt
- Build the semantic layer and the shared metrics the whole team relies on, readable by our agents and Claude Code skills as well as by people
- Test and document your models so the team can trust them
- Specify the events and properties we are missing, for the engineering team to implement
After 3 months
- Our core performance metrics are modelled, tested and exposed in the semantic layer
- Campaign performance has measurably improved: you ship changes every week, each one measured by A/B tests
- The update loop runs through Claude Code skills the team can rerun
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