Asset & Wealth Management - Quantitative Strategist -VP - London

Goldman Sachs
London
2 months ago
Applications closed

Related Jobs

View all jobs

Alpha Data Services, Performance Ready Data Analyst, EMEA Lead, Vice President

Data Analyst – Asset Optimisation

Data Analyst

Data Analyst – Asset Optimisation

Systematic Credit Quantitative Researcher

Systematic Credit Quantitative Researcher

Job Description

GOLDMAN SACHS

At Goldman Sachs, we connect people, capital, and ideas to help solve problems for our clients. We are a leading global financial services firm providing investment banking, securities and investment management services to a substantial and diversified client base that includes corporations, financial institutions, governments, and individuals.

ASSET MANAGEMENT

Goldman Sachs Asset Management delivers innovative investment solutions through a global, multi-product platform that offers clients the advantages that come with working with a large firm, while maintaining the benefits of a boutique. We are a top 10 global asset manager with a leadership position across asset classes and key market segments. Our success is driven by a global team of talented professionals who collaborate to deliver innovative client solutions.

QUANTITATIVE STRATEGISTS

Quantitative strategists work in close collaboration with bankers, traders, and portfolio managers on complex financial and technical challenges. We work on alpha generating strategies; discuss portfolio allocation problems; and build models for prediction, pricing, trading automation, data analysis and more. The strats platform is designed for people to express themselves by providing creative solutions to business problems. Strats own analytics, models for pricing, return and risk, as well as portfolio management platform.

Responsibilities

As a quantitative strategist your responsibilities will include:

  • Working with revenue-generating businesses to solve a broad range of problems, including quantitative strategy development, quantitative modelling, portfolio construction, portfolio optimization, infrastructure development and implementation, financial product and markets analytics
  • Develop quantitative analytics and signals using advanced statistical, quantitative, or econometric techniques to improve portfolio construction process and implement fund management models to track longer term portfolio performance
  • Develop sustainable production systems, which can evolve and adapt to changes in our fast-paced, global business environmen
  • Provide quantitative analytics to optimize investment structure, pricing, returns and capital sourcing
  • Partner globally across multiple divisions and engineering teams to create quantitative modeling-based solutions
  • Prioritize across competing problems, communicate with key stakeholders

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

© The Goldman Sachs Group, Inc., 2023. All rights reserved.

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

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.

Careers

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.

Jobs

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.

Education

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.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.