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

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 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.

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, and other areas of the Global Markets Division, and directly with clients.

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 Franchise. 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. You will utilize technologies including Python (Pandas, Polars, Scikit‐learn, TensorFlow/PyTorch), Jupyter, Trino, SQL, and gain exposure to graph database technologies.

• 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.

• 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.