At a Glance
- Tasks: Design and implement advanced machine learning models to transform financial markets.
- Company: Join Goldman Sachs, a leading global investment banking firm with a focus on innovation.
- Benefits: Enjoy competitive salary, health insurance, and opportunities for professional growth.
- Other info: Collaborative environment with excellent career advancement opportunities.
- Why this job: Make a real impact by leveraging AI to enhance client experiences in finance.
- Qualifications: Advanced degree in a quantitative field and 2+ years of hands-on data science experience.
The predicted salary is between 60000 - 80000 £ per year.
Are you a visionary Data Scientist or Machine Learning Scientist passionate about leveraging cutting‑edge AI to transform financial markets? Do you thrive on building end‑to‑end solutions, from robust data pipelines and sophisticated feature engineering to deploying advanced predictive models and personalized recommendation systems? If you have hands‑on experience, and a desire to make a significant impact in a dynamic, fast‑paced environment, we want to hear from you.
OUR IMPACT
The Global Markets Division
As a Data Scientist/Machine Learning Scientist in the Global Markets Division, you will be at the forefront of innovation on the trading floor. You will design and implement advanced machine learning models, including predictive AI, to uncover complex market trends and generate actionable insights. Leveraging cutting‑edge techniques, you will develop sophisticated analytical tools that will, at scale, connect clients to the signals, tools and expertise to better analyze their portfolios and manage risk. Your expertise will drive data‑driven product development and business strategy through advanced analytics, predictive modelling, and the application of recommendation systems.
HOW YOU WILL FULFILL YOUR POTENTIAL
At Goldman Sachs, our Engineers and Scientists don’t just make things – we make things possible. Change the world by connecting people and capital with ideas and technology. Combine advanced engineering and deep market knowledge to solve the most pressing problems for our clients. We look for creative collaborators who evolve, adapt to change, and thrive in a fast‑paced global environment.
Your work will directly enhance the client experience and drive strategic decision‑making. You will collaborate closely with Traders, Salespeople, and Strats across all asset classes, leveraging your expertise to build robust data pipelines, engineer impactful features, and train sophisticated models. Your contributions will be critical in developing personalized recommendation engines and predictive analytics that drive Marquee platform adoption and ensure clients receive the most relevant, timely, and actionable content.
RESPONSIBILITIES
- Design, build, and maintain robust data pipelines for feature engineering and model training, ensuring data quality, scalability, and explainability.
- Develop, train, and deploy state‑of‑the‑art machine learning models, with a strong focus on recommendation systems and signal generation, to address complex problems in financial markets.
- Utilize and contribute to the development of graph databases and knowledge graphs to enrich data context, uncover hidden relationships, and make predictions.
- Conduct rigorous model evaluation, A/B testing, and monitoring to ensure optimal performance, reliability, and business impact of deployed models.
- Collaborate with product managers, engineers, and business stakeholders to translate complex analytical findings into clear, actionable insights and integrate ML solutions into production systems.
- Stay abreast of the latest advancements in machine learning, AI, and data science, and proactively identify opportunities to apply new techniques.
- Contribute to the team's overall technical growth and best practices.
QUALIFICATIONS
- Advanced degree (Master’s or Ph.D.) in Computer Science, Machine Learning, Statistics, Applied Mathematics, or a related quantitative field.
- 2+ years of proven expertise and hands‑on experience in the full lifecycle of data science and machine learning projects, from data ingestion and feature engineering to model deployment and monitoring in a production environment.
- Exceptional programming skills in Python, with a command of data science libraries (e.g., Pandas, NumPy, Scikit‑learn).
- Experience with big data technologies (e.g., Spark, Trino, Hadoop) and ideally cloud platforms (e.g., AWS, GCP, Azure) for scalable data processing, storage, and model deployment.
- Ideally having understanding and practical experience with graph databases (e.g., Neo4j, Amazon Neptune, ArangoDB) and knowledge graph construction, querying (e.g., Cypher, SPARQL), and utilization for feature enrichment.
- Strong understanding of statistical modeling, experimental design, and causal inference.
- Clear, critical thinking, concise writing skills, and excellent communication skills, with the ability to articulate complex technical concepts to diverse audiences.
- Self‑starter with a creative, hands‑on approach to problem‑solving and a passion for designing and implementing programmatic solutions to client needs.
- Able to thrive in a global, fast‑paced, and collaborative team environment.
StudySmarter Expert Advice🤫
We think this is how you could land Global Banking & Markets - Data / Machine Learning Scientist, Marquee Sales Strats, Associate in London
✨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!
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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 Global Banking & Markets - Data / Machine Learning Scientist, Marquee Sales Strats, Associate at Goldman Sachs Bank AG.
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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.
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We think you need these skills to ace Global Banking & Markets - Data / Machine Learning Scientist, Marquee Sales Strats, Associate in London
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.
