Senior AI Data Scientist, Agentic Automation (Marketing)

ph3Company Overview /h3 pteam.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. /p h3Company Overview /h3 pteam.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. /p h3Position Overview /h3 pWe are looking for a senior data scientist to streamline marketing operations at team.blue, by building agentic systems to run them. You would report into the Applied AI team and work on marketing automation projects. /p pMarketing 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. /p pThe 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. /p h3What We Are Actually Screening For /h3 pClassical ML and applied statistics are the entry fee for this role, necessary and assumed. Everyone we are talking to has them. /p pFour things separate candidates. /p pbCan you build an agent someone should trust. /b 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. /p pbCan you tell whether the data underneath is worth reasoning over. /bThese 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 at scale. Part of the work is refusing to build on a source until it is trustworthy, and saying so with evidence. /p pbCan you reshape a request. /b 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. /p pbCan you take it to production yourself. /b 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 productionise, this is the wrong role, and no amount of modelling depth compensates. /p h3Your day would involve /h3 ul liSitting 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 /li liExtracting requirements live from people who do not think in data models /li liDesigning 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 /li liBuilding 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 /li liDeciding where a human stays in the loop, at what confidence threshold, and designing a review queue marketers will open a second time /li liWriting evals for output that precision and recall do not capture: is the diagnosis correct, is the cited source real and does it say what the agent claims, does a generated brief hold brand voice in Greek and Dutch as well as in English /li liChecking whether the tracking data an agent depends on is sound before building on it, and quantifying the error when it is not /li liWiring an agent to a webhook or a scheduled trigger, and making the handler idempotent so a retry does not double-post a recommendation or apply the same keyword exclusion twice /li liDeciding which steps in a flow warrant a frontier model and which run on something cheap, then proving that routing decision with numbers, because these flows run daily across many brands and the bill compounds /li liSitting in a vendor demo asking what their API actually exposes, what the rate limits and quotas are, what their data model looks like, and what integration really costs us /li /ul h3What You Will Bring /h3 ul li7+ years building data and ML systems in industry, spanning both sides of the LLM shift. We want the judgment that comes from having debugged systems before you could ask a model what was wrong. /li liSomewhere in that history: you have been the only person who did a job end to end. First or only data hire, the single ML person in a small company, or a one-person function inside a large one. We are less interested in company stage than in the condition, because it is what forces someone to map the process, build it, deploy it and answer for it rather than hand each part to a specialist. /li liSomewhere in that history: you have shipped something with permission to act on live systems affecting real customers, and you can tell us what you did to sleep at night. /li liExpert in Python and ML. /li liYou ship end to end. Python someone else can still read in six months, a current toolchain (uv, Docker or an equivalent, we care that you re-examine your tooling, not which tool you landed on), your own container, your own instrumentation. /li liProduction experience with multi-step, tool-calling LLM workflows: orchestration, retries, idempotency, timeouts, partial-failure recovery. State-machine design, not only train/serve pipelines. /li liIntegration against third-party APIs you do not control. Auth flows, rate limits, pagination, sandbox behaviour that differs from production, and schema changes shipped without notice. /li liCost and latency engineering as a first-class concern: model routing, caching, batching, and the instinct to know what a flow costs per run before Finance asks. /li liA safety instinct for systems that take actions: staging modes, approval gates, least-privilege scoping, a way back. /li liEvaluation design for generative and agentic output: LLM-as-judge, golden-transcript regression suites, red-teaming. Including calibration: an agent that reports high confidence needs to be right at that rate, and you can show whether it is. /li liProcess mapping and quantification. You can sit with a domain expert, capture what actually happens rather than what the policy says, and attach hours to it. /li liTechnical vendor evaluation. Judging a martech vendor on API surface, data model, extensibility and true integration cost, not on the sales deck. /li /ul h3Nice to have /h3 ul liMaster’s or PhD in Computer Science, AI, Machine Learning or a related field /li liPromptOps at scale: versioning, testing and rollback of prompts and models as production artefacts /li liPrior exposure to martech, ad-tech or SEO tooling and their APIs, or to automation in any domain where output is customer-facing /li liExperience evaluating generated output across multiple languages /li /ul h3Right to work /h3 pAt any stage, please be prepared to provide proof of eligibility to work in the country you are applying for. Unfortunately, we are unable to support relocation packages or sponsor visas. /p h3Come as you are /h3 pEveryone is welcome here. Diversity and inclusion are at our core. Far above any technical competence, we value respect, openness, and trusted collaboration. We do not tolerate intolerance. /p h3ESG /h3 pAt team.blue, our commitment to caring for the environment and each other is at the heart of everything we do. Our latest impact report showcases our ongoing ESG efforts and ambitious sustainability goals. Interested in learning more about our dedication to making a positive impact? Check it out here. /p h3The most trusted digital enabler /h3 pteam.blue is a leading digital enabler for companies and entrepreneurs. It serves over 3.3 million customers in Europe and has more than 3,000 experts to support them. Its goal is to shape technology and to empower businesses with innovative digital services. /p pbClick here to read more about team.blue /b /p /p #J-18808-Ljbffr