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Senior Data Scientist
📍 Location: London, United Kingdom
🏢 Industry: Information Services
💼 Work Setting: Hybrid
Are you passionate about advancing the future of AI-powered scientific discovery through large language models, retrieval systems, and intelligent research assistants?
We are seeking a Senior Data Scientist I to join a centralized Platform Data Science organization focused on building the AI foundation behind scientific discovery and research platforms. This role will play a key part in developing next-generation AI experiences, including LLM-powered assistants, semantic search systems, agentic workflows, retrieval-augmented generation (RAG), and AI evaluation frameworks.
The ideal candidate combines expertise in Generative AI, NLP, Information Retrieval, Machine Learning, Search Systems, and Applied AI Research with the ability to rapidly prototype and transform cutting-edge AI innovations into scalable production solutions.
Key Responsibilities
Applied AI Research & Development
- Design, prototype, and develop advanced AI solutions for scientific research workflows.
- Build intelligent systems that support:
- Scientific Question Answering
- Literature Summarization
- Semantic Discovery
- Research Insight Generation
- Citation-Aware Reasoning
- Evaluate and implement emerging AI techniques and architectures.
- Translate research concepts into scalable product capabilities.
- Drive innovation across AI-powered discovery experiences.
Large Language Models (LLMs) & Generative AI
- Develop and optimize LLM-powered applications.
- Design workflows utilizing:
- Foundation Models
- Prompt Engineering
- Context Management
- Grounding Strategies
- Hallucination Mitigation
- Integrate LLMs with trusted scientific and scholarly content.
- Improve the accuracy, reliability, and trustworthiness of AI-generated outputs.
- Evaluate emerging model providers, architectures, and orchestration patterns.
Agentic AI & Intelligent Workflows
- Design and implement agent-based AI systems.
- Build multi-step reasoning workflows using tools such as:
- LangGraph
- LangChain
- Workflow Orchestration Frameworks
- Enable intelligent decision-making and task execution capabilities.
- Improve automation of complex research and discovery processes.
- Develop reusable patterns for agentic AI applications.
Search, Retrieval & Knowledge Discovery
- Design and optimize enterprise-scale retrieval architectures.
- Develop:
- Lexical Search Systems
- Vector Search Solutions
- Hybrid Retrieval Architectures
- Improve relevance and search effectiveness through advanced ranking methods.
- Support semantic exploration of scientific knowledge.
- Enable efficient discovery across large-scale research repositories.
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Retrieval-Augmented Generation (RAG)
- Build and enhance RAG pipelines that combine LLMs with scientific and biomedical content.
- Optimize:
- Embedding Strategies
- Retrieval Workflows
- Re-Ranking Models
- Chunking Techniques
- Context Selection Methods
- Improve answer quality, grounding, and factual accuracy.
- Support production deployment of AI-assisted research solutions.
NLP & Semantic AI
- Apply state-of-the-art techniques in:
- Natural Language Processing
- Semantic Search
- Embeddings
- Knowledge Representation
- Language Understanding
- Develop semantic enrichment solutions that improve content discoverability.
- Integrate metadata, ontologies, and domain knowledge into AI systems.
- Support knowledge-driven research and discovery experiences.
AI Evaluation & Experimentation
- Design and maintain evaluation frameworks for search and AI systems.
- Measure performance using:
- NDCG
- Recall
- Precision
- Grounding Metrics
- Faithfulness Metrics
- Hallucination Detection
- Build benchmark datasets and evaluation pipelines.
- Conduct offline and online experiments to assess model performance.
- Support A/B testing and continuous optimization initiatives.
Responsible AI & Quality Assurance
- Promote responsible AI principles across product development.
- Evaluate:
- Model Reliability
- Explainability
- Trustworthiness
- Answer Accuracy
- User Impact
- Implement strategies that improve transparency and safety.
- Support development of trusted AI research experiences.
Production AI & Platform Development
- Partner with engineering teams to productionize AI and search capabilities.
- Develop scalable AI pipelines and workflows.
- Support deployment of production-ready AI systems.
- Contribute to platform architecture and long-term technical strategy.
- Ensure AI solutions are reliable, maintainable, and scalable.
Cross-Functional Leadership
- Collaborate closely with:
- Product Managers
- Software Engineers
- UX Researchers
- Data Scientists
- Domain Experts
- Translate complex technical concepts into actionable business recommendations.
- Influence AI product roadmaps and strategic direction.
- Provide technical leadership across AI initiatives.
Qualifications
Education
- Required
- Master's Degree or PhD in:
- Computer Science
- Data Science
- Artificial Intelligence
- Machine Learning
- Information Retrieval
- Natural Language Processing
- Related Technical Discipline
- Master's Degree or PhD in:
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Experience
- Required
- Significant experience in:
- Applied AI
- Machine Learning
- NLP
- Information Retrieval
- Generative AI
- Experience building production-grade AI systems.
- Experience developing search and retrieval solutions at scale.
- Experience translating research into production applications.
- Significant experience in:
Technical Skills
Artificial Intelligence & Machine Learning
- Large Language Models (LLMs)
- Generative AI
- Machine Learning
- Deep Learning
- AI Reasoning Systems
- Retrieval-Augmented Generation (RAG)
NLP & Search
- Natural Language Processing (NLP)
- Semantic Search
- Information Retrieval
- Search Ranking
- Vector Databases
- Embeddings
Agentic AI
- LangGraph
- LangChain
- Agent Workflows
- AI Orchestration Frameworks
- Multi-Step Reasoning Systems
Programming
- Python (Advanced)
- Data Science Libraries
- Production AI Development
- AI & ML Platforms
- Databricks
- Distributed Computing Platforms
- ML Development Environments
- Experiment Tracking Systems
Frameworks & Tools
- PyTorch
- Hugging Face
- LangGraph
- LangChain
- Haystack
Evaluation & Analytics
- A/B Testing
- Statistical Analysis
- Evaluation Framework Design
- Benchmark Development
- Experimental Design
- Visualization
- Tableau
- Power BI
- Matplotlib
- Seaborn
Preferred Qualifications
- Experience building:
- AI Assistants
- Conversational AI Platforms
- Agentic AI Systems
- Experience with:
- Recommendation Engines
- Search Platforms
- Ranking Systems
- Knowledge of:
- Scientific Research Content
- Biomedical Datasets
- Scholarly Publishing
- Experience with:
- Knowledge Graphs
- Ontologies
- Semantic Enrichment Platforms
- Exposure to:
- MLOps
- Production ML Systems
- AI Governance
- Publications or applied research in:
- NLP
- Information Retrieval
- Search
- Generative AI
Core Competencies
- Generative AI
- Large Language Models (LLMs)
- Retrieval-Augmented Generation (RAG)
- Information Retrieval
- Search Engineering
- Agentic AI
- LangGraph
- LangChain
- Natural Language Processing (NLP)
- Semantic Search
- AI Evaluation
- Experimentation & A/B Testing
- Knowledge Graphs
- Machine Learning
- Python Development
- Databricks
- Scientific AI Applications
- Applied AI Research
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Skills
Generative AI
Large Language Models
Retrieval-Augmented Generation
Natural Language Processing
Information Retrieval
Agentic AI
Python
PyTorch
LangChain
LangGraph
Vector Databases
Semantic Search
Machine Learning
Deep Learning
AI Evaluation
Databricks
Location