Senior AI Data Scientist, Agentic Automation; Marketing

Company Overview

team.blue is the market leader in enabling digital success for small and medium-sized businesses (SMBs) across Europe, catering to over 3 million customers in 25+ languages. Our mission is to make online business success simpler, by providing our customers with all the tools and resources they need to excel online and remain ahead of the curve.

team.blue is the market leader in enabling digital success for small and medium-sized businesses (SMBs) across Europe, catering to over 3 million customers in 25+ languages. Our mission is to make online business success simpler, by providing our customers with all the tools and resources they need to excel online and remain ahead of the curve.

We are looking for a senior data scientist to streamline marketing operations e, by building agentic systems to run them. You would report into the Applied AI team and work on marketing automation projects.

Marketing here runs across many brands, markets and languages, on a stack that differs brand by brand. The work spans competitive and pricing monitoring, performance reporting and diagnosis, SEO and AI-answer visibility, content refresh, localisation and lifecycle production, paid search and social account hygiene, and tracking and consent QA. Each of these is a multi-step process across several systems, repeated per brand.

The method you will follow matters more than the domain: map processes, quantify the time and resources they consume, determine the ROI impact of agentic automation, build a proof of concept, take it to production and measure the impact of your work. This is closer to building autonomous, business-impact systems than to building pure single purpose models.

Classical ML and applied statistics are the entry fee for this role, necessary and assumed. Everyone we are talking to has them.

Four things separate candidates.

Can you build an agent someone should trust. Most of these agents produce a judgment, backed by numbers: this page lost traffic because of a SERP change, this brand under-converts relative to a comparable one, this competitor’s pricing move matters. A confidently wrong judgment gets read and acted on across several markets before anyone checks it. Lots of this is irreversible, so a recommendation nobody can reconstruct the reasoning for is worse than no recommendation, because it costs trust.

Calibration, provenance and auditable workflows are key components of the systems we develop, not a compliance layer on top of them.

Can you tell whether the data underneath is worth reasoning over. These agents read from analytics, search console, ad platforms, CRM and third-party SEO and social tools. Tracking is inconsistent across brands, UTM conventions are followed unevenly, and tags break silently. An agent built on that without checking will generate fluent nonsense t of the work is refusing to build on a source until it is trustworthy, and saying so with evidence.

Can you reshape a request. You will be handed requests written by domain experts, and some of them will be the wrong shape: an agent asked to do something a query would do better, or scoped to advise where it could act. We need someone who can understand that, explain better ways to structure the process, and propose a version that works, rather than building what was asked and shipping a thing nobody uses.

Can you take it to production yourself. We mean end to end literally. You write it, you containerise it, you instrument it, and you deploy it with minimal guidance from the devops teams. If the last three things you built were Jupyter notebooks handed to someone else to product ionise, this is the wrong role, and no amount of modelling depth compensates.

• Sitting with an SEO or paid search owner and mapping how a traffic-drop investigation actually runs today across brands, then attaching hours per week to each step of it
• Extracting requirements live from people who do not think in data models
• Designing the state transitions: what triggers, what branches, which APIs get called, where it waits for a human, and what happens when step 4 of 9 fails or a vendor rate-limits you mid-run
• Building the guardrails before the capability: dry-run mode, an approval gate ahead of anything that writes to a live account or publishes externally, least-privilege API scopes, a documented undo
• Deciding where a human stays in the loop, at what confidence threshold, and…