Global Banking & Markets - Data Scientist / Machine Learning Scientist, Marquee Sales Strats - Associate

Goldman Sachs
London
3 months ago
Create job alert

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 insightsLeveraging 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. Our data scientists and machine learning engineers are applying advanced quantitative and AI/ML techniques to solve the most complex business challenges in a dynamic, entrepreneurial team with a passion for the markets.

Marquee 

Marquee is Goldman Sachs' premier digital platform for Global Banking & Markets, serving our Institutional Clients (Hedge Funds, Asset Managers, Insurers) and Corporate Clients with the latest insights and analytics from the division. A recognized market leader, Marquee has garnered 5 awards over the past 3 years for its innovative solutions.

The Marquee Sales Strats Team 

The Marquee Sales Strats team is a hub for advanced data science and machine learning, focusing on developing and deploying predictive AI, recommendation systems, and sophisticated analytical models. We leverage extensive datasets, including those structured in graph databases and knowledge graphs, to generate deep insights into financial markets and enhance Marquee’s platform engagement. We collaborate closely with Sales, Trading, Engineering, Product, Design, other areas of the Global Markets Division, and directly with clients. Our global team comprises experts in financial markets, product structuring, cutting-edge technology, and advanced data science/machine learning, including specialists in graph theory and knowledge representation.

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.

As a Marquee Sales Data Scientist/Machine Learning Scientist, you will be instrumental in designing, developing, and deploying advanced machine learning models, including predictive AI and recommendation systems, to deliver unparalleled analytics and insights for the Goldman Sachs FranchiseYour 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. You will utilize technologies including Python (Pandas, Polars, Scikit-learn, TensorFlow/PyTorch), Jupyter, Trino, SQL, and gain exposure to graph database technologies.

RESPONSIBILITIES AND QUALIFICATIONS:

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 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 (., Pandas, NumPy, Scikit-learn) 
* Experience with big data technologies (., Spark, Trino, Hadoop) and ideally cloud platforms (., AWS, GCP, Azure) for scalable data processing, storage, and model deployment.

* Ideally having understanding and practical experience with graph databases (., Neo4j, Amazon Neptune, ArangoDB) and knowledge graph construction, querying (., 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.

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 /careers. We’re committed to finding reasonable accommodations for candidates with special needs or disabilities during our recruiting process.

Related Jobs

View all jobs

Applied Research - Artificial Intelligence - London - VP

Applied Research - Artificial Intelligence - London - Associate

Senior Lead Analyst - Data Science_ AI/ML & Gen AI

Data Scientist Placement

Tech Audit Manager, Vice President – Commercial & Investment Banking Data Management and Artificial Intelligence

Principal Data Scientist I - Agentic Systems

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

What Hiring Managers Look for First in AI Job Applications (UK Guide)

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

The Skills Gap in AI Jobs: What Universities Aren’t Teaching

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.