Junior Data Scientist UK at Satalia

London, England, United Kingdom -

Full Time

Start Date

Immediate

Expiry Date

28 Jul, 26

Salary

0.0

Posted On

29 Apr, 26

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, Machine learning, Data science, Pandas, NumPy, Scikit-learn, PyTorch, TensorFlow, LLMs, Prompt engineering, Data cleaning, Feature engineering, Git, SQL, Causal inference, Retrieval-augmented generation

Industry

technology;Information and Media

Description

Junior Data Scientist Role type: Full time Location: UK (Fully remote) Preferred start date: ASAP About Satalia 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. The Role 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. This is a hands-on role where you will learn by working alongside experienced data scientists on real production systems that serve global clients. We invest heavily in developing our junior hires and will pair you with senior mentors who will help you grow into a strong, independent practitioner. What You'll Do Explore and prepare datasets — cleaning, feature engineering, and exploratory analysis across structured and unstructured data (text, image, tabular). Train and evaluate ML models under the guidance of senior scientists, learning how to move from a working prototype to a production-ready system. Write and maintain Python code that runs in production — scripts, pipeline components, and data processing jobs — with support through code review. Help build and test components of LLM-powered systems: prompt templates, evaluation scripts, data loaders, and retrieval pipelines. Run experiments systematically: track hypotheses, log results, and communicate findings clearly to the team. Learn and adopt software engineering best practices — Git workflows, testing, documentation, and CI/CD — as part of your daily work. What We're Looking For A degree in a quantitative field (computer science, mathematics, statistics, physics, engineering, or similar) or equivalent practical experience. Solid understanding of ML fundamentals: supervised vs. unsupervised learning, overfitting, evaluation metrics, and basic model selection. Working knowledge of Python — you can write functions, use libraries, debug errors, and read other people's code. Familiarity with core data science libraries (pandas, NumPy, scikit-learn). Exposure to PyTorch or TensorFlow is a plus. Some project experience with ML — academic projects, personal projects, internships, or competition entries all count. Show us something you've built. Curiosity and initiative — you read papers, follow releases, tinker with new tools, and ask good questions. Clear communication — you can explain what you did, why, and what you learned from it. Nice to Have Exposure to deep learning (NLP or computer vision) through coursework or personal projects. Familiarity with Git and command-line workflows. Experience with SQL or any data pipeline tooling. Interest in LLMs, prompt engineering, or generative AI — even if it's just personal experimentation. Contributions to open-source projects, Kaggle competitions, or a technical blog. What we Offer Benefits - enhanced pension, life assurance, income protection, private healthcare; Remote working - café, bedroom, beach - wherever works; Truly flexible working hours - school pick up, volunteering, gym; Generous Leave - 27 days oliday plus bank holidays and enhanced family leave; Annual bonus - when Satalia does well, we all do well; 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. We aim to respond to all applications within 2 weeks. If you have not heard from us within 2 weeks this means your application has been unsuccessful. By applying to Satalia you are expressly giving your consent for the collection and use of your information as described within our Satalia Recruitment Privacy Policy. Good luck!

Responsibilities

You will explore and prepare datasets while training and evaluating machine learning models under the guidance of senior scientists. Additionally, you will write and maintain production-ready Python code and contribute to the development of LLM-powered systems.