Senior Data Scientist, Operations (GenAI)
- AI-Ready Data Development: Design, develop, and maintain high-quality AI-ready datasets, ensuring data integrity, usability, and scalability to support advanced Generative AI models.
- Advanced Data Processing: Lead hands-on efforts in complex data extraction, cleansing, and curation for diverse text and numerical datasets. Implement sophisticated metadata enrichment strategies to enhance data utility and accessibility for AI systems.
- Algorithm Implementation & Optimization: Implement and optimize state-of-the-art algorithms and pipelines for efficient data processing, feature engineering, and data transformation tailored for LLM and GenAI applications.
- GenAI Application Development: Apply and integrate frameworks like LangChain and Hugging Face Transformers to build modular, scalable, and robust Generative AI data pipelines and applications.
- Prompt Engineering Application: Apply advanced prompt engineering techniques to optimize LLM performance for specific data extraction, summarization, and generation tasks, working closely with the Lead's guidance.
- LLM Evaluation Support: Contribute to the systematic evaluation of Large Language Models (LLMs) outputs, analysing quality, relevance, and accuracy, and supporting the implementation of LLM-as-a-judge frameworks.
- Retrieval-Augmented Generation (RAG) Contribution: Actively contribute to the implementation and optimization of RAG systems, including working with embedding models, vector databases, and, where applicable, knowledge graphs, to enhance data retrieval for GenAI.
- Technical Mentorship: Act as a technical mentor and subject matter expert for junior data scientists, providing guidance on best practices in coding, data handling, and GenAI methodologies.
- Cross-Functional Collaboration: Collaborate effectively with global data science teams, engineering, and product stakeholders to integrate data solutions and ensure alignment with broader company objectives.
- Operational Excellence: Troubleshoot and resolve data-related issues promptly to minimize potential disruptions, ensuring high operational efficiency and responsiveness.
- Documentation & Code Quality: Produce clean, well-documented, production-grade code, adhering to best practices for version control and software engineering.
- Academic Background: Advanced degree in AI, statistics, mathematics, computer science, or a related field.
- Programming and Frameworks: Deep experience with Python, TensorFlow or PyTorch, and NLP libraries such as spaCy and Hugging Face.
- GenAI Tools: Practical experience with LangChain, Hugging Face Transformers, and embedding models for building GenAI applications.
- Prompt Engineering: Deep expertise in prompt engineering, including prompt tuning, chaining, and optimization techniques.
- LLM Evaluation: Experience evaluating LLM outputs, including using LLM-as-a-judge methodologies to assess quality and alignment.
- RAG and Knowledge Graphs: Practical understanding and experience using vector databases. In addition, familiarity with graph-based RAG architectures and the use of knowledge graphs to enhance retrieval and reasoning would be a strong plus.
- Cloud: Practical experience with Gemini/OpenAI models and comfortable with cloud platforms such as AWS, Google Cloud, or Azure. Proficient with Docker for containerization.
- Data Engineering: Strong understanding of data extraction, curation, metadata enrichment, and AI-ready dataset creation.
- Collaboration and Communication: Excellent communication skills and a collaborative mindset, with experience working across global teams.
- Competitive salary and company bonus scheme
- Group pension scheme
- Group healthcare and life assurance scheme
- Flexible working environment
- 25 days holiday with annual increase up to 30 days
- Subsidised gym membership
- Season ticket travel loans
- Cycle to work scheme
- Extensive internal and external training
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