Data Scientist Lead

Job Description

As an innovative data scientist in . Morgan Asset Management's Data Science team, you will design and implement ML solutions to enhance investment processes, elevate client experiences, and streamline operations. Initially, you will be focused on developing solutions to support our ESG and Stewardship functions with a heavy focus on content extraction, search and principals-based reasoning with LLMs. Your technical expertise will drive impactful results, and you’ll play a key role in shaping our data science capabilities.

You’ll thrive in a collaborative culture that values hands-on problem solving and continuous learning.

Your technical expertise will drive impactful results, and you’ll play a key role in shaping our data science capabilities. You’ll thrive in a collaborative culture that values hands-on problem solving and continuous learning.

• Collaborate with internal stakeholders to understand business needs, build out requirements, and design technical architectures
• Develop technical solutions utilising LLMs with a focus on problems involving search, content extraction and principal-based reasoning
• Build comprehensive evaluation packages to ensure the efficacy and reliability of solutions and to build trust with stakeholders
• Help to design technical architectures and solutions
• Collaborate heavily with engineering functions to deliver high quality, scalable output
• Stay up to date with the latest developments in AI and become an SME within the data science function

• Advanced degree (MS or PhD) in a quantitative or STEM discipline or significant practical experience in industry.
• Commercial experience in applying NLP, LLM and ML techniques in solving high-impact business problems, such as semantic search, information extraction, question answering, personalisation, classification, recommendation or forecasting.
• Advanced python programming skills with experience writing production quality code using ML libraries and deep learning frameworks.
• Good understanding of the foundational principles and practical implementations of ML algorithms such as clustering, decision trees, deep learning, reinforcement learning, etc.
• Strong knowledge of NLP, language modelling, prompt engineering, and domain adaptation.
• Ability to communicate complex concepts and results to both technical and business audiences.

• Strong analytical skills with an understanding of financial markets and asset management line of business
• Strong business domain knowledge in ESG, investment stewardship, or buyside investment
• Familiarity with techniques for model explainability and self-validation
• CFA or equivalent financial qualification