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 division's 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., Databricks, Snowflake); specialized in transforming complex, unstructured datasets into structured, optimized formats engineered specifically to train and scale predictive models.