Global Banking & Markets, Structured Credit (SFL) Desk Strat, Associate, London

Goldman Sachs
London
1 year ago
Applications closed

Related Jobs

View all jobs

Data Engineer

Data Analyst

Data Analyst - Sales Operations

Genomic Data Scientist in Rare Disease (we have office locations in Cambridge, Leeds & London)

Principal, AI Data Science

Senior Data Engineer

What we do

Structured Finance & Lending (SFL) Strats Team within Global Markets Division (GMD) is responsible for modeling and pricing of structured trades, as well as building risk management tools for SFL businesses and clients using cutting edge quantitative, machine learning, and other AI techniques. The business focuses on providing customized financing solutions to clients, which covers a wide range of collateral asset classes such as private credit and equity, capital calls or specialty assets, in the forms of Loans, Repurchase Agreements (Repos), Asset-Backed Securities and Derivatives. This role offers a unique opportunity to work within the Structured Financing and Lending businesses to deliver tailored solutions to our clients while gaining exposure to a wide range of asset classes.

Your Impact

SFL Strats play a critical role in deal structuring, pricing, execution and risk management. This is a highly visible platform to put quantitative skills and knowledge in use to make a direct impact on business growth. You will gain familiarity with different asset classes & risk factors while working on various trades and projects and build a broad foundation of product knowledge.

Responsibilities

Improve existing pricing models and create new ones for structured products. Understand transaction risks and analyze drivers of profits and losses. Provide analysis for new transactions. Drive commercial outcomes using data. Improve existing and create new models for the pricing and analysis of derivatives, public/private market assets and transactions Identify, curate, and integrate new structured and unstructured datasets into models. Build end to end solutions from data collection to automated actions.

Who We Look For

Strong quantitative and coding skills with desire to develop commercial mindset Solid work ethics, team oriented, high levels of motivation. Ability to work in fast-paced environment and time-sensitive situations. Effective communication skills in verbal and writing to both technical and business audience.

Basic Qualifications

Excellent academic record in a relevant quantitative field such as Mathematics, Physics, Engineering or Computer Science. Strong math and quantitative skills Experience in object-oriented programming with a language such as C++, Java or Python. Knowledge of Stochastic calculus and derivatives pricing, or Machine Learning background Knowledge of credit market and products, interest rates, FX, or risk management is preferred.

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.

Where to Advertise Machine Learning Jobs in the UK (2026 Guide)

Advertising machine learning jobs in the UK requires a different approach to most technical hiring. The candidate pool is small, highly specialised and in demand across AI labs, financial services, healthcare, autonomous systems and consumer technology simultaneously. Machine learning engineers and researchers move between roles through professional networks, conference communities and specialist platforms — not general job boards where ML roles compete with unrelated software engineering positions for the same audience. This guide, published by MachineLearningJobs.co.uk, covers where to advertise machine learning roles in the UK in 2026, how the main platforms compare, what employers should expect to pay, and what the data says about hiring across different role types.

New Machine Learning Employers to Watch in 2026: UK and Global Companies Driving ML Innovation

Machine learning (ML) has transitioned from a specialised field into a core business capability. In 2026, organisations across healthcare, finance, robotics, autonomous systems, natural language processing, and analytics are expanding their machine learning teams to build scalable intelligent products and services. For professionals exploring opportunities on www.MachineLearningJobs.co.uk , understanding the companies that are scaling, winning investment, or securing high‑impact contracts is crucial. This article highlights the new and high‑growth machine learning employers to watch in 2026, focusing on UK innovators, international firms with significant UK presence, and global platforms investing in machine learning talent locally.

How Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.