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Senior Data Scientist

1 month ago

London, Greater London, United Kingdom LexisNexis Intellectual Property Solutions Full time

About the Role

We are seeking a Senior GenAI/Data Scientist to join our AI Innovation Team. This role will focus on experimenting, optimising and applying Generative AI off the shelf models to extract valuable insights from large-scale patent datasets and enhance our search and analytics tools. You will collaborate with data scientists, product teams, and stakeholders across different geographies, driving innovation through LLMs (Large Language Models) and advanced AI methodologies.

Responsibilities:

  • Break down complex business problems into actionable AI solutions, leading the design and development of Generative AI models.
  • Work closely with cross-functional teams to identify key areas for AI-driven innovation in patent search and analytics applications.
  • Collaborate with the team to explore Generative AI use cases, including automated summarisation, natural language understanding, and text generation.
  • Ensure solutions are scalable, maintainable, and aligned with best practices in machine learning.
  • Work on GenAI techniques like Prompt Engineering, RAG (Retrieval-Augmented Generation) and perform evaluation using frameworks to optimise LLM performance.
  • Develop and implement machine learning workflows, focusing on the integration of GenAI with existing data infrastructure.
  • Perform continuous evaluations and improvements of models to handle large volumes of patent data.
  • Collaborate with data engineers and data scientists to integrate AI models seamlessly into the broader data architecture.
  • Provide mentorship and coaching to junior team members, fostering a learning culture within the team.

Requirements:

  • Demonstrate 4+ years of experience in data science, with a focus on NLP, Generative AI and LLMs.
  • Proficiency in Python and experience working with LLMs and NLP frameworks (e.g. Hugging Face, Spacy, Pytorch/Tensorflow etc).
  • Experience with Prompt Engineering, RAG techniques and various evaluation methodologies for integrating GenAI with search/retrieval systems and measure the quality.
  • Experience with LangChain / LlamaIndex, vector databases (e.g., FAISS), fine-tuning models on domain-specific data.
  • Experience working with cloud platforms like Azure, AWS, or GCP for machine learning workflows.
  • Understanding of data engineering pipelines and distributed data processing (e.g., Databricks, Apache Spark).
  • Strong analytical skills, with the ability to transform raw data into meaningful insights through AI techniques.
  • Experience with SQL, ETL processes, and data orchestration tools (e.g. Azure Data Factory, Talend) will be an advantage