Senior Data Scientist II
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What you'll do
- Develop and implement NLP, LLM, and generative AI approaches (e.g., RAG, prompt strategies).
- Define agentic workflows and reasoning strategies for multi-step legal tasks.
- Develop retrieval strategies, including hybrid search (semantic + lexical), and evaluation metrics (e.g., relevance, ranking quality).
- Analyze large-scale legal datasets to extract insights and improve model performance.
- Establish best practices for model evaluation, validation, and benchmarking.
- Translate experimental results into clear product recommendations and business impact.
- Collaborate with product, legal experts, and engineers to align solutions with user needs.
- Mentor team members and provide technical leadership in data science and AI.
What they're looking for
- Master’s degree or above in a quantitative or technical field (Statistics, Computer Science, Mathematics, Data Science, etc.).
- Strong experience in machine learning, NLP, and LLM-based modeling.
- Strong experience designing and running experiments, including model evaluation and iteration.
- Experience with generative AI techniques (e.g., prompt engineering, RAG).
- Experience designing and evaluating hybrid search (semantic + lexical) using embeddings and vector databases.
- Experience designing agentic workflows and reasoning strategies, with hands-on experience applying agent frameworks (e.g., LangChain, LangGraph, AutoGen) in real-world use cases.
- Proficiency in Python and data analysis tools.
- Strong foundation in statistics, modeling, and large-scale text processing.
Summarised by NextRaise from the employer’s description, which follows in full below.
Full description from employer
About the Team:
LexisNexis Legal & Professional, which serves customers in more than 150 countries with 11,800 employees worldwide, is part of RELX (www.relx.com), a global provider of information-based analytics and decision tools for professional and business customers. Our company has been a long-time leader in deploying AI and advanced technologies to the legal market to improve productivity and transform the overall business and practice of law, deploying ethical and powerful generative AI solutions with a flexible, multi-model approach that prioritizes using the best model from today’s top model creators for each individual legal use case. The company employs over 2,000 technologists, data scientists, and experts to develop, test, and validate solutions in line with RELX Responsible AI Principles (https://stories.relx.com/responsible-ai-principles/index.html).
About the Role:
Are you looking to develop your Data Scientist career?
Would you enjoy training more junior team members?
We are seeking a Senior Data Scientist II to help lead the design and validation of AI-driven product capabilities within the legal domain. This team is global AI platform team that builds AI products and ships AI features to production. The team works on cutting edge AI technology and extensively leverages latest AI tools/Features as part of product.
Responsibilities:
Develop and implement NLP, LLM, and generative AI approaches (e.g., RAG, prompt strategies).
Define agentic workflows and reasoning strategies for multi-step legal tasks.
Develop retrieval strategies, including hybrid search (semantic + lexical), and evaluation metrics (e.g., relevance, ranking quality).
Analyze large-scale legal datasets to extract insights and improve model performance.
Establish best practices for model evaluation, validation, and benchmarking.
Translate experimental results into clear product recommendations and business impact.
Collaborate with product, legal experts, and engineers to align solutions with user needs.
Mentor team members and provide technical leadership in data science and AI.
Requirements:
Master’s degree or above in a quantitative or technical field (Statistics, Computer Science, Mathematics, Data Science, etc.).
Strong experience in machine learning, NLP, and LLM-based modeling.
Strong experience designing and running experiments, including model evaluation and iteration.
Experience with generative AI techniques (e.g., prompt engineering, RAG).
Experience designing and evaluating hybrid search (semantic + lexical) using embeddings and vector databases.
Experience designing agentic workflows and reasoning strategies, with hands-on experience applying agent frameworks (e.g., LangChain, LangGraph, AutoGen) in real-world use cases.
Proficiency in Python and data analysis tools.
Strong foundation in statistics, modeling, and large-scale text processing.
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