Data Scientist Greece Mid Level

Satalia builds enterprise-grade AI systems for WPP and its FTSE 100 client base. Led by WPP Chief AI Officer Daniel Hulme, we run as a high-autonomy, decentralised organisation where engineers and scientists own their domains end to end. We are building AI systems that operate on terabyte-scale multimodal datasets to power the next generation of marketing intelligence., Our current work includes: Agentic pipelines - multi-step LLM systems with tool use, planning, and self-evaluation that automate complex marketing workflows end to end. Domain-adapted foundation models - fine-tuning open-weight LLMs (LoRA, RLHF, distillation) on proprietary WPP data for tasks like audience segmentation, creative scoring, and brand-safety classification. Retrieval-augmented generation - production RAG systems over large proprietary corpora (embedding models, vector indices, re-ranking) that serve real-time answers to client queries. Classical ML at scale - gradient-boosted models, causal inference pipelines, and recommendation engines that run alongside LLM components in hybrid architectures. You will work as part of an experienced team contributing directly to one or more of these workstreams. You’ll have real ownership of your work - building models, running experiments, and shipping code to production - with guidance from senior scientists who will help you grow technically.

What You’ll Do Build and iterate on ML models - from data exploration and feature engineering through training, evaluation, and deployment. Implement and maintain components of production ML pipelines: data pre-processing, model serving, monitoring, and retraining workflows. Contribute to LLM-powered systems - building prompt chains, evaluation harnesses, RAG pipelines, or fine-tuning workflows. Analyse large multimodal datasets (text, image, video, structured metadata) to extract features and insights that feed downstream models. Write clean, tested, production-quality Python code - not just notebooks. Participate in code reviews, design discussions, and experiment retrospectives.

2-4 years of experience building ML models, with at least some of that work deployed to production. Solid fundamentals in machine learning: you understand bias-variance trade-offs, cross- validation, regularisation, and can reason about why a model is underperforming. Working proficiency in Python and comfort with core ML libraries. Exposure to at least one of: NLP/LLMs, computer vision, recommender systems, or time- series modelling. Strong experience with software engineering practices - Git, testing, CI/CD, code review. Curiosity about LLMs and modern AI tooling. Clear communication - you can explain your modelling choices and results to both technical and non-technical colleagues.

Nice to Have Experience with LLM fine-tuning, prompt engineering, or RAG systems. Familiarity with cloud infrastructure (AWS/GCP), Docker, and orchestration tools. Background in marketing technology, ad tech, or audience modelling. A relevant MSc or PhD, or equivalent depth from self-directed learning and project work.

• Truly flexible working hours - school pick up, volunteering, gym;
• Generous Leave - holidays in line with Greek Law, plus bank holidays and enhanced family leave;
• Impactful projects - focus on bringing meaningful social and environmental change;
• People oriented culture - wellbeing is a priority, as is being a nice person;
• Transparent and open culture - you will be heard;
• Development - focus on bringing the best out of each other;

Satalia is home to some of the brightest minds in AI and if you’re looking to join a company who not only values autonomy and freedom, but embraces a culture of inclusion and warmth, we’d love to hear from you.