Global Banking & Markets - Quantitative Engineering - Analyst / Associate - London London · Uni[...]

Goldman Sachs Bank AG
City of London
2 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Architect

Enterprise Market Risk Quantitative Analyst (IRRBB & CSRBB), AVP

Senior Data Engineer AWS - Finance Consultancy

Data Analyst

Data Analyst

Data Analyst Senior Consultant, Assistant Manager, Manager - Belfast

Global Banking & Markets - Quantitative Engineering - Analyst / Associate - London location_on London, Greater London, England, United Kingdom


At Goldman Sachs, quantitative strategists are renowned for their expertise in building and developing quantitative and technological solutions to tackle complex analytical challenges. As a Portfolio Analytics strategist, you'll collaborate closely with trading desks and the business to define, implement, and manage all the analytics required to enhance decision-making and maintain a competitive edge. This includes real-time pricing, risk analytics, large-scale analysis, and optimizations, along with addressing technical challenges in a scalable manner. Being a strategist means being at the heart of the action on the trading floor, staying aware of and reacting to market environments, and working closely with key business stakeholders.


Your impact

As a portfolio analytics strategist, you'll work with trading desks and desk strategists to integrate pricing models into state-of-the-art risk management tools. You'll be a key partner for the business on all risk management topics. This unique position sits at the intersection of mathematical models and real-life implementation, allowing you to make a significant impact by mastering the connection between the two. Strategists are at the core of business activities, constantly collaborating with colleagues from multiple regions and adjacent analytical teams to develop scalable, cutting-edge technology.


Responsibilities

  • Develop cutting edge risk management capabilities, providing fast and reliable tools for different desks and businesses.
  • Perform systematic and quantitative analysis of different markets and implement the most optimal risk calculations accordingly.
  • Work closely with trading and providing support for the risk management systems.
  • Be involved with all stages of the software development life cycle with a range of technologies and collaborate closely with engineering teams who support the underlying infrastructure and frameworks.

Qualifications

  • Excellent academic background in a quantitative field such as mathematics, physics, statistics, or computer science. A major in computer science with an interest in quantitative topics, or a quantitative background with a strong interest in implementation, is preferred.
  • Strong programming skills in an object oriented or functional paradigm such as C++, Java or Python.
  • Self-starter with strong self-management skills, ability to manage multiple priorities and work in a high-paced environment.
  • Excellent written and verbal communication skills.
  • Experience up in finance or a cutting-edge technology company is a plus.
  • Experience in building risk management systems (irrespective of asset class) is also a plus.
  • Previous quantitative or technical role working on or with a derivatives trading desk (irrespective of asset class) is also a plus.

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 GS.com/careers.


We’re committed to finding reasonable accommodations for candidates with special needs or disabilities during our recruiting process. Learn more: https://www.goldmansachs.com/careers/footer/disability-statement.html


Goldman Sachs is an equal opportunity employer and does not discriminate on the basis of race, color, religion, sex, national origin, age, veterans status, disability, or any other characteristic protected by applicable law.


#J-18808-Ljbffr

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 Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

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

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

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

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.