Asset Management, Data Scientist, Vice President, London/Glasgow

The candidate must excel in working in a highly collaborative environment together with the business stakeholders, technologists, and control partners to deploy solutions into production. A strong passion for machine learning is essential, along with a commitment to continuous learning, research, and experimentation with new innovations in the field. Candidates should possess a solid understanding of modern NLP and/or financial knowledge, hands-on implementation experience, strong analytical thinking, and a keen interest in applying advanced analytics to solve complex problems in finance and asset management. 

 

Job Responsibilities 

• Design and implement agentic AI workflows that automate and streamline complex business processes.
• Design and apply  advanced techniques such as semantic search, retrieval-augmented generation (RAG), named entity recognition (NER), prompt engineering, and personalization for content extraction, search, question answering, reasoning, and recommendation. 
• Develop LLM, NLP, and ML solutions that address client requirements and drive business transformation. 

• Work closely with partner teams—including Business, Technology, Product Management, Strategy, and Business Management—to deploy and scale developed models in production environments. 

• Build comprehensive testing setups to evaluate model performance and ensure efficacy and reliability. 

• Communicate results effectively to business stakeholders through written reports, visualizations, and presentations. 

• Stay current with the latest research in LLM, agentic AI, ML, and data science, and proactively identify and leverage emerging techniques to drive ongoing enhancement. 

 

Qualifications, Capabilities, and Skills 

• Proven experience applying NLP, LLM, and ML techniques to solve high-impact business problems. 
• Demonstrated intellectual curiosity and a passion for staying current with the latest advancements in AI, LLMs, agentic AI frameworks, and data science.
• Proficiency in programming languages such as Python and familiarity with machine learning libraries and frameworks. 

• Excellent communication skills and ability to work collaboratively in a fast-paced, dynamic environment. 

  • Prior experience in financial services or asset management is desirable, along with a strong interest in applying AI to the financial domain.