Data Science Senior Associate - LLM/NLQ - Asset Management Data Analytics

Are you passionate about data science and eager to make a real impact in asset management? As an NLQ/LLM Data Scientist, you’ll help transform investment processes and client experiences with innovative natural language and machine learning solutions. You’ll collaborate with talented teams, continuously learn, and drive meaningful change using the latest data science techniques. Join us to advance your career and shape the future of investment management.

As an NLQ/LLM Data Scientist in the Asset Management Data & Analytics team, you will design and implement natural language interfaces that enhance decision-making and optimize operational processes. You will work closely with business stakeholders, technologists, and control partners to deploy solutions into production. Your expertise will generate actionable insights and improve client experiences, while you stay at the forefront of data science innovation.

Job Responsibilities:

• Collaborate with internal stakeholders to identify business needs and develop NLQ solutions that drive transformation
• Apply large language models, machine learning techniques, and statistical analysis to enhance decision-making and workflow efficiency
• Collect and curate datasets for evaluation and continuous improvement
• Perform data science experiments, such as building model architectures, hyperparameter tuning, and evaluations
• Monitor and improve model performance through feedback and active learning
• Work with technology teams to deploy and scale models in production
• Deliver written, visual, and oral presentations of modeling results to stakeholders
• Stay current with research in LLM, ML, and data science, leveraging emerging techniques for ongoing enhancement

Required Qualifications, Capabilities, and Skills:

• Degree in a quantitative or technical discipline, or practical industry experience
• Proficient Python programming skills with production-quality coding experience
• Experience working with structured and unstructured data
• Experience in prompt engineering and domain adaptation
• Understanding of foundational ML algorithms such as clustering and decision trees
• Ability to communicate complex concepts and results to technical and business audiences
• Active interest in applying ML solutions to investment management

Preferred Qualifications, Capabilities, and Skills:

• Experience in data science roles such as data engineering, ML engineering, LLM engineering, or data analytics
• Proficiency with SQL and Snowflake
• Experience in Asset Management
• Experience applying NLP, LLM, and ML techniques to solve business problems such as semantic search, information extraction, question answering, summarization, personalization, classification, or forecasting