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Senior Data Scientist
Senior Data Scientist
📍 Location: London Area, United Kingdom | 🏢 Industry: Informatics services | 💼 Work Setting: Hybrid
About the Role
Are you a Senior Data Scientist with a passion for driving innovation in intelligent search, retrieval, knowledge discovery, and generative AI? This role is seeking an expert to lead the design and deployment of next-generation AI-powered solutions leveraging large language models (LLMs), retrieval-augmented generation (RAG), semantic search, and agentic workflows. The ideal candidate will combine deep expertise in machine learning, NLP, and generative AI with the ability to translate cutting-edge research into scalable, production-ready products.
Key Responsibilities
Applied AI & Generative AI Development
- Lead the design, prototyping, and implementation of AI-powered workflows and applications, including:
- Question answering
- Content summarization
- Knowledge discovery
- Insight generation
- Context-aware reasoning
- Build and optimize solutions leveraging:
- Large Language Models (LLMs)
- Prompt engineering
- Agentic AI frameworks
- Multi-step reasoning workflows
- Evaluate emerging AI technologies to identify opportunities for innovation and product enhancement.
Search, Retrieval & RAG Systems
- Design and optimize enterprise search and retrieval architectures.
- Develop Retrieval-Augmented Generation (RAG) frameworks combining trusted data sources with LLM capabilities.
- Improve search relevance and answer quality through:
- Vector search
- Semantic search
- Hybrid retrieval methods
- Embeddings optimization
- Re-ranking models
- Retrieval orchestration
- Build scalable workflows for information discovery and knowledge exploration.
AI Evaluation & Experimentation
- Design, implement, and maintain evaluation frameworks for AI and search systems.
- Develop methodologies to measure:
- Search effectiveness
- Retrieval performance
- Response quality
- Grounding accuracy
- Hallucination mitigation
- Trustworthiness and reliability
- Conduct:
- Offline experiments
- Benchmark evaluations
- A/B testing initiatives
- Training dataset creation and validation.
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Machine Learning & NLP Innovation
- Apply advanced techniques in:
- Machine Learning
- Natural Language Processing (NLP)
- Semantic representation learning
- Knowledge modeling
- Optimize model performance through experimentation and iterative testing.
- Support scalable AI and ML capabilities aligned with business goals.
Knowledge & Semantic Systems
- Integrate structured and unstructured data into intelligent AI workflows.
- Leverage metadata, ontologies, taxonomies, and semantic assets to enhance knowledge discovery.
- Support semantic enrichment and contextual understanding across applications.
- Contribute to knowledge management and information architecture.
Cross-Functional Collaboration
- Partner across disciplines, including:
- Product managers
- Software engineers
- UX researchers
- Domain experts
- Data scientists
- Translate complex technical insights into actionable business recommendations.
- Support AI product strategy, roadmap development, and innovation.
- Ensure prototype-to-production transitioning of AI solutions.
Responsible AI & Quality Assurance
- Promote responsible AI practices, ensuring:
- Model reliability
- Transparency
- Fairness
- Trustworthiness
- Performance monitoring
- Guide governance and evaluation strategies for AI systems.
Qualifications
Required
- Master’s or PhD in:
- Computer Science
- Data Science
- Machine Learning
- Natural Language Processing
- Information Retrieval
- Related quantitative discipline
- Significant experience in:
- Applied AI
- Machine Learning
- NLP
- Search Technologies
- Information Retrieval Systems
- Strong programming skills in Python.
Technical Expertise
- Hands-on experience with:
- Large Language Model (LLM) applications
- Generative AI solutions
- Retrieval-Augmented Generation (RAG) systems
- Semantic search platforms
- Recommendation and ranking systems
- Proficiency in:
- PyTorch
- Hugging Face
- LangChain & LangGraph
- Haystack
- Other AI/ML frameworks
- Experience with distributed data science and ML platforms.
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Search & Retrieval Skills
- Thorough understanding of:
- Search architectures
- Lexical retrieval
- Vector search
- Hybrid search systems
- Embedding models
- Retrieval optimization techniques
- Practical experience improving relevance, ranking, and information discovery.
Analytics & Experimentation
- Expertise in:
- Experimental design
- Statistical analysis
- Evaluation frameworks
- Benchmark testing
- Performance metrics
- Experience creating analytics dashboards and visualizations using modern tools.
Preferred Qualifications
- Experience building:
- AI assistants
- Conversational AI solutions
- Agentic AI workflows
- Intelligent research platforms
- Familiarity with:
- Knowledge graphs
- Ontologies
- Semantic enrichment technologies
- Recommendation engines
- Applied knowledge of:
- Scientific/research/healthcare/biomedical/scholarly datasets
- Production deployment and support of ML systems.
- MLOps and AI platform engineering experience.
Publications & Research Contributions
- Preferred prior research or publications in:
- NLP
- Information retrieval
- Search technologies
- Generative AI
- Machine learning systems
Core Competencies
- Applied AI & Generative AI
- Large Language Models (LLMs)
- Retrieval-Augmented Generation (RAG)
- Natural Language Processing (NLP)
- Information Retrieval & Search
- Machine Learning
- Semantic Search & Knowledge Discovery
- Experimentation & Evaluation
- AI Product Innovation
- Data Science & Analytics
- Python Development
- Cross-functional collaboration
- Responsible AI
- Technical leadership
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Skills
Machine Learning
Natural Language Processing
Information Retrieval
Generative AI
Large Language Models
Retrieval-Augmented Generation
Semantic Search
Python
AI Evaluation
Experimentation
Cross-Functional Collaboration
Responsible AI
Data Science
Analytics
Search Technologies
Knowledge Discovery
Location