At a Glance
- Tasks: Join our team to design and implement AI-driven models for investment processes.
- Company: Goldman Sachs, a leader in finance and technology innovation.
- Benefits: Competitive salary, diverse work environment, and opportunities for professional growth.
- Other info: Collaborative culture with exposure to high-value commercial problems.
- Why this job: Make a real impact using cutting-edge data science and AI technologies.
- Qualifications: MSc or PhD in a quantitative field with strong programming skills.
The predicted salary is between 60000 - 80000 £ per year.
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-scoped 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.
Data Engineering employer: Goldman Sachs Bank AG
Goldman Sachs is an exceptional employer, offering a dynamic work environment where innovation thrives and employees are empowered to drive meaningful change in the investment landscape. With a strong focus on professional development, team collaboration, and cutting-edge AI initiatives, employees have ample opportunities for growth and to make a tangible impact. Located in a vibrant financial hub, the company fosters a culture of inclusivity and excellence, making it an ideal place for talented individuals seeking a rewarding career in data science.
StudySmarter Expert Advice🤫
We think this is how you could land Data Engineering
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Goldman Sachs Bank AG!
✨Show Off Your Projects
Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Data Engineering at Goldman Sachs Bank AG.
✨Leverage Professional Networks
Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Goldman Sachs Bank AG.
✨Apply Directly through Our Website
When you find a suitable opening like Data Engineering at Goldman Sachs Bank AG, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!
We think you need these skills to ace Data Engineering
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Goldman Sachs Bank AG, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Goldman Sachs Bank AG. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Goldman Sachs Bank AG
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Goldman Sachs Bank AG!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
