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Data Scientist
📍 Location: London, United Kingdom
🏢 Industry: Information Services
💼 Work Setting: Hybrid
Are you passionate about leveraging Artificial Intelligence, Machine Learning, and advanced analytics to build innovative solutions that transform how users discover, access, and interact with information?
We are seeking a talented Data Scientist to design, develop, and optimize advanced AI-powered solutions that enhance search, knowledge discovery, intelligent retrieval, and decision-support capabilities. This role offers the opportunity to work with modern AI technologies, including Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Natural Language Processing (NLP), and advanced machine learning techniques to deliver scalable, production-ready applications.
Key Responsibilities
Applied AI & Machine Learning
- Develop and enhance AI-powered applications that support intelligent information retrieval, knowledge discovery, question answering, summarization, and insight generation.
- Build and improve multi-step AI workflows and intelligent automation solutions using modern orchestration frameworks.
- Apply advanced techniques in machine learning, natural language processing, generative AI, semantic search, and information retrieval.
- Contribute to prompt engineering, context optimization, grounding strategies, and AI output quality improvements.
- Evaluate emerging AI tools, models, and technologies, providing recommendations for adoption and innovation.
- Integrate structured and unstructured data sources into intelligent AI workflows to improve system performance and relevance.
Search, Retrieval & Intelligent Knowledge Systems
- Design and optimize search and retrieval systems using lexical, semantic, vector, and hybrid search approaches.
- Develop and enhance Retrieval-Augmented Generation (RAG) architectures to improve response quality and accuracy.
- Experiment with embeddings, ranking models, retrieval strategies, and relevance optimization techniques.
- Build knowledge discovery solutions that enable users to efficiently access and explore information.
- Collaborate with engineering teams to deploy, scale, and monitor AI-driven solutions in production environments.
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AI Evaluation & Experimentation
- Design evaluation frameworks to measure the effectiveness, reliability, accuracy, and relevance of AI and search systems.
- Develop benchmark datasets, testing methodologies, and performance measurement processes.
- Conduct experiments, model evaluations, and controlled testing initiatives to validate solution effectiveness.
- Analyze results and provide actionable recommendations to improve AI model performance and user outcomes.
- Support responsible AI initiatives focused on trust, transparency, quality, and risk mitigation.
Cross-Functional Collaboration
- Partner with product managers, engineers, designers, researchers, and business stakeholders to deliver impactful AI solutions.
- Translate complex technical findings into clear and actionable insights for technical and non-technical audiences.
- Contribute to best practices, knowledge sharing, and continuous improvement initiatives within the data science organization.
- Support projects through the full lifecycle, from research and prototyping to deployment and optimization.
Required Qualifications
Education
- Master's degree or Ph.D. in Computer Science, Data Science, Artificial Intelligence, Machine Learning, Statistics, Information Retrieval, Natural Language Processing, or a related quantitative field.
Experience
- Approximately 2 to 4 years of professional experience in Data Science, Machine Learning, Artificial Intelligence, Natural Language Processing, Information Retrieval, or related disciplines.
- Experience developing and deploying AI-powered applications in real-world or production environments.
- Proven ability to independently execute technical projects and collaborate across cross-functional teams.
- Experience conducting experimentation, model evaluation, and performance analysis.
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Technical Skills
- Strong experience with:
- Large Language Models (LLMs)
- Generative AI Applications
- Retrieval-Augmented Generation (RAG)
- Natural Language Processing (NLP)
- Semantic Search and Information Retrieval
- Search Ranking and Relevance Optimization
- Vector Databases and Embedding Models
- Prompt Engineering and Context Management
- Machine Learning and Statistical Analysis
- Python Programming
- Experience with modern AI and machine learning frameworks such as:
- PyTorch
- TensorFlow
- Hugging Face
- LangChain
- LangGraph
- Haystack
- Distributed Machine Learning Platforms (e.g., Databricks or similar)
- Experience using data visualization and analytics tools such as Tableau, Power BI, Matplotlib, Seaborn, or equivalent platforms.
- Ability to communicate insights through dashboards, reports, and visual storytelling techniques.
Preferred Qualifications
- Experience building AI assistants, conversational AI solutions, or agent-based workflows.
- Knowledge of search, recommendation, ranking, or retrieval systems.
- Familiarity with knowledge graphs, ontologies, semantic enrichment, or metadata-driven applications.
- Exposure to production machine learning systems, MLOps practices, and model deployment frameworks.
- Research or practical experience in NLP, information retrieval, search technologies, or generative AI.
- Experience working with large-scale content-rich, knowledge-intensive, or regulated data environments.
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