Global Banking & Markets, IB Strats, VP, London

Goldman Sachs
London
1 year ago
Applications closed

Related Jobs

View all jobs

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

Staff / VP, Machine Learning Engineer (UK)

Staff / VP, Data Scientist (UK)

Machine Learning Manager

Machine Learning Engineer

Data Scientist

Overview

Investment Banking Strats (“IB Strats”) are active in marketing, structuring, pricing and executing transactions for large corporations and institutions in the capital markets and M&A. We develop state-of-the-art quantitative and analytical methods for advising clients and building financing and risk-management solutions across products and industries.

IB Strats are both investment bankers and innovators that create analytics and scalable technology platforms that will shape the future of investment banking and how we connect with clients. Direct participation in client interactions, deal executions and other core commercial activities allows us to be in the optimal position to develop technical innovations that create economic leverage and differentiate the firm.

Our team members are financial engineers and data scientists who share a passion for investment banking and the financial markets.

Primary Responsibilities

The Financing Group within the Global Banking and Markets Division is looking to hire a Vice President in London for the Equity Capital Markets Strats Team. This Strat will work closely with the Equity Capital Markets group, which originates, structures, and executes equity financings and solutions on behalf of Goldman Sachs's global corporate client base. This Strat will develop analyses and tools to help originate equity deals through targeting and marketing, and to optimize deal execution. The work involves financial modeling, statistical data analysis techniques, algorithmic problem solving, and machine learning to identify and solve business problems. The role will require close collaboration with Strats in Financing Group globally.

Preferred Qualifications

Bachelor’s or advanced degree in a quantitative or engineering discipline At least 5 years of prior experience in the financial industry, preferably in a capital markets role Familiarity with corporate finance, financial modeling, and financial engineering Familiarity with statistical techniques, including theory and programming implementation Strong quantitative / analytical reasoning and problem-solving abilities  Strong technical and computer programming skills with proficiency in programming languages such as C++/JAVA/Python Strong oral and written communication skills Strong interest in finance, investment banking, and the capital markets 
 

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.

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.