Goldman Sachs
Asset & Wealth Management - Data Scientist - Associate - London

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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
Reasons to use Rodeo
I’m in my final year doing Economics and I don’t know whether to apply for grad schemes now or do a masters first. What do you think?
Honest answer — it depends on where you want to end up. A lot of top grad schemes (Big 4, civil service, banking) don’t need a masters. Let’s look at the ones you’d be competitive for now, and we can decide if a masters actually adds anything.
Also worth knowing: most autumn 2026 applications are open now. Timing matters more than you think.
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Graduate Consultant — 2026 Scheme
Why you're a good match
StrongYour economics background and your summer at a regional bank line up with what PwC looks for on the consulting scheme. Applications close in four weeks.
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Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.
Why you're a good match
You’ve got the grades and the economics background, and your bank internship is exactly the experience this scheme looks for. Apply soon — deadlines close within the month.
Experience fit
Your summer at the bank plus your econometrics coursework map directly to the day-one responsibilities on this scheme — client modelling, market briefings, and deal support.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
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.


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ABOUT GOLDMAN SACHS
At Goldman Sachs, we commit our people, capital and ideas to help our clients, shareholders and the communities we serve to grow. Founded in 1869, we are a leading global investment banking, securities and investment management firm. Headquartered in New York, we maintain offices around the world.
We believe who you are makes you better at what you do. We're committed to fostering and advancing diversity and inclusion in our own workplace and beyond by ensuring every individual within our firm has a number of opportunities to grow professionally and personally, from our training and development opportunities and firmwide networks to benefits, wellness and personal finance offerings and mindfulness programs. Learn more about our culture, benefits, and people at GS.com/careers.
We’re committed to finding reasonable accommodations for candidates with special needs or disabilities during our recruiting process. Learn more: https://www.goldmansachs.com/careers/footer/disability-statement.html
© The Goldman Sachs Group, Inc., 2023. All rights reserved. Goldman Sachs is an equal opportunity employer and does not discriminate on the basis of race, color, religion, sex, national origin, age, veterans status, disability, or any other characteristic protected by applicable law.
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