Asset & Wealth Management Data Scientist Associate London

Join our Alternatives Data Science team and contribute to DSML and AI initiatives across the full lifecycle of the investment process. The Data Scientist will be responsible for the design development and implementation of data- and AI-driven models to drive innovation and productivity for origination due diligence and investment performance. The data science team sits alongside the Goldman Sachs Deal Teams and works closely with the Goldman Sachs Value Accelerator and portfolio company management teams.

Key Responsibilities:

• Leverage sophisticated statistical mathematical and programming skills to analyse complex datasets support the investment processes and drive quantifiable commercial value
• Partner with Deal Teams to identify high-value commercial problems and translate them into well-scope technical solutions
• Own the end-to-end delivery of prototypes through an investment lens from framing the commercial problem and sourcing alternative datasets to exploring the data and building the underlying model or pipeline that powers the solution
• Partner strategically with portfolio company management teams to drive data and AI initiatives for value creation
• Partner with GS Engineering to lead development and implementation of data-centric and AI tools enhancing our investment processes and supporting our deal and fundraising teams
• Stay up-to-date with the latest developments in AI ML and related fields to continuously improve the divisions data and AI capabilities

Qualifications experience and attributes:

• MSc or PhD in a quantitative field such as Mathematics Statistics Physics Engineering Computer Science or a related field
• 2 years of relevant experience applying quantitative methods to commercial problems with measurable impact
• Strong programming skills (Python SQL) and experience using the basic data science libraries (e.g. pandas scikit-learn) and comfort writing clean modular code beyond notebooks
• High-level of proficiency in mathematics statistics and data science theory
• Proven experience implementing sophisticated data science techniques handling large datasets translating data into actionable business insights. Experience with alternative data is advantageous
• Commercial experience with a strong track record of quantitative problem solving and realised commercial impact
• Excellent written and verbal communication and collaboration skills with a strong growth mindset

Highly valued:

• Hands-on experience building with modern AI tooling including LLMs prompt engineering RAG pipelines embeddings vector databases and at least one agent or orchestration framework (e.g. LangChain LlamaIndex LangGraph)
• Experience with cloud platforms (AWS Azure GCP) and basic familiarity with Docker APIs and lightweight web frameworks (FastAPI Streamlit) for shipping prototypes
• Exposure to private equity investment banking consulting or operating roles in portfolio companies
• Experience working in embedded or client-facing delivery models (consulting forward deployed solutions engineering) supporting data-informed decision making
• Familiarity with LLM evaluation frameworks and responsible AI practices
• Adept at designing high-performance schemas and feature stores within modern cloud data platforms (e.g. DatabricksSnowflake); specialized in transforming complex unstructured datasets into structured optimized formats engineered specifically to train and scale predictive models.