Controllers - London - Vice President - Quantitative Engineering

Goldman Sachs
London
1 year ago
Applications closed

Related Jobs

View all jobs

Powertrain Software Engineer

Machine Learning Engineer for Game Technology

Marketing Machine Learning Engineer

Machine Learning Research Scientist | Generative Models | Protein Design | Deep Learning | Pyth[...]

Full Stack Developer

Machine Learning Research Engineer | Generative Models | Protein Design | Deep Learning | Pytho[...]

What We Do

At Goldman Sachs, our Engineers don’t just make things – we make things possible. Change the world by connecting people and capital with ideas. Solve the most challenging and pressing engineering problems for our clients. Join our engineering teams that build massively scalable software and systems, architect low latency infrastructure solutions, proactively guard against cyber threats, and leverage machine learning alongside financial engineering to continuously turn data into action. Create new businesses, transform finance, and explore a world of opportunity at the speed of markets.

The Controllers division is responsible for financial control and regulatory obligations of the firm. They safeguard the assets of the firm through an independent scrutiny of the financial information and ensure accurate reporting to internal and external consumers. They provide critical metrics and related analysis to the firm’s and divisions’ leadership to navigate the evolving business strategy, including incumbent and strategic initiatives. Controllers play an important role in the changing landscape of the firm, including its new business ventures and acquisitions, and ensure that these new initiatives are in line with the regulatory expectations as well as controlled in terms of their incorporation into the firm

Finance engineers help ensure the firm meets all of its financial control and reporting obligations. Working in small and nimble teams, we build critical and complex software to calculate profit and loss (P&L), independently verify valuations, measure and optimize the firm’s capital, balance sheet and liquidity metrics, and regulatory filings across the globe.

OUR Impact:

Controllers Strats is responsible for designing and implementing solutions to manage the firm’s P&L, independently verify valuations, measure and optimize the firm’s capital, balance sheet and liquidity metrics, and regulatory obligations. Our global agile teams (based across Americas, EMEA and Asia) develop and manage the platforms, calculation engines, and analytical tools that controllers, risk management, and deal-making teams use to project, monitor and report externally to regulatory for both regular business activity and under stress scenarios.

YOUR Impact:

We conduct our business in increasingly complex markets. Our people must continually find new ways to provide access to capital, manage risk and provide investment opportunities for our clients to enable them to realize their goals. We judge ourselves on our ability to help clients anticipate and respond to changing market conditions and to create opportunities that merit the trust they place in us.

Controllers Strats is a multidisciplinary group of quantitative experts within the Controllers Division, focusing on independent price verification, regulatory capital measurement, revenue analysis and modelling. The group is primarily responsible for building advanced quantitative models and analytical tools for valuation risk and regulatory capital pertaining to the three areas mentioned. In this role, you will leverage your technical skills and functional expertise in P&L, balance sheet or regulatory capital (Basel 3) to build new calculations for the firm’s books and records and for new regulatory capital rules released as part of Basel 3 Endgame. The position would provide a unique opportunity to drive one of the most impactful initiatives at the firm and to directly engage with colleagues and senior management across revenue areas, Risk, and Engineering.

Why join the team? 

Broad exposure to pricing and calibration models for a variety of financial products, including derivatives, illiquid cash products, private equity, etc. Exposure to challenging quantitative problems such as modeling risks for derivatives, large scale Monte-Carlo simulations of complete portfolios across the firm, fast and accurate approximate valuation risk measurements. Exposure to machine learning and data science skills, and applications in finance. Gain understanding of evolving regulatory framework and leverage quantitative skills to help the firm manage capital resources. Interpersonal Communication: You’ll engage with business users and engineers across all areas of the business to understand their requirements and to propose solutions tailored to their needs. Autonomy: You’ll have significant autonomy in designing and writing solutions to help our stakeholders deliver for the firm’s clients. Creativity: You’ll be encouraged to suggest improvements to products and to propose ways in which we can add value for our stakeholders. Training: Your manager will support your professional development, allowing you time for training at work, helping you learn and grow within the organization, and providing opportunities for increasing responsibility. 

RESPONSIBILITIES AND QUALIFICATIONS

Develop quantitative models in 3 areas Independent price verification models that govern key business strategies and decisions related to valuation of products including complex derivatives and hard to value private investments Revenue analysis and modelling that governs new activity review, valuation adjustments and sign-off of daily P&L for all market making desks Regulatory Capital models for key externally reported capital metrics that play a key role in determining forward-looking business strategies and decisions in an evolving regulatory landscape Provide ongoing testing and support for existing models Documentation and quality control of models Work in a dynamic, fast-paced environment that provides exposure to all areas of Finance Build strong relationships with business partners Identify opportunities for cross-divisional collaboration and reuse of common solutions Provide technical and functional guidance and leadership to junior members on a need basis

 SKLLS AND EXPERIENCE WE ARE LOOKING FOR

PhD or Master’s candidate in a quantitative field such as mathematics, physics, statistics or engineering 4+ years experience in financial modeling Excellent command of mathematics, modeling and numerical algorithms. Exposure to machine learning and data science skills, and applications in finance is a plus. Strong programming skills and experience with an object oriented programming language (such as C++, Python, or Java) Excellent communication skills including experience speaking to technical and business audiences and working globally Comfortable with multi-tasking, managing multiple stakeholders and working as part of a team Experience building pricing and risk models or familiarity with capital, stress testing and resolution planning

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 .

We’re committed to finding reasonable accommodations for candidates with special needs or disabilities during our recruiting process.

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Portfolio Projects That Get You Hired for AI Jobs (With Real GitHub Examples)

In the fast-evolving world of artificial intelligence (AI), an impressive portfolio of projects can act as your passport to landing a sought-after role. Even if you’ve aced interviews in the past, employers in AI and machine learning (ML) are increasingly asking candidates to demonstrate hands-on experience through the projects they’ve built and shared online. This is because practical ability often speaks volumes about your suitability for a role—far more than any exam or certification alone could. In this article, we’ll explore how to build an outstanding AI portfolio that catches the eye of recruiters and hiring managers, including: Why an AI portfolio is crucial for job seekers. How to choose AI projects that align with your target roles. Specific project ideas and real GitHub examples to help you stand out. Best practices for showcasing your work, from writing clear READMEs to using Jupyter notebooks effectively. Tips on structuring your GitHub so that employers can instantly see your value. Moreover, we’ll discuss how you can use your portfolio to connect with top employers in AI, with a handy link to our CV-upload page on Artificial Intelligence Jobs for when you’re ready to apply. By the end, you’ll have a clear roadmap to building a portfolio that will help secure interviews—and the AI job—of your dreams.

AI Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

In today's competitive AI job market, nailing a technical interview can be the difference between landing your dream role and getting lost in the crowd. Whether you're looking to break into machine learning, deep learning, NLP (Natural Language Processing), or data science, your problem-solving skills and system design expertise are certain to be put to the test. AI‑related job interviews typically involve a range of coding challenges, algorithmic puzzles, and system design questions. You’ll often be asked to delve into the principles of machine learning pipelines, discuss how to optimise large-scale systems, and demonstrate your coding proficiency in languages like Python, C++, or Java. Adequate preparation not only boosts your confidence but also reduces the likelihood of fumbling through unfamiliar territory. If you’re actively seeking positions at major tech companies or innovative AI start-ups, then check out www.artificialintelligencejobs.co.uk for some of the latest vacancies in the UK. Meanwhile, this blog post will guide you through 30 real coding & system-design questions you’re likely to encounter during your AI job interview. This list is designed to help you practise, anticipate typical question patterns, and stay ahead of the competition. By reading through each question and thinking about the possible approaches, you’ll sharpen your problem-solving skills, time management, and critical thinking. Each question covers fundamental concepts that employers regularly test, ensuring you’re well-equipped for success. Let’s dive right in.

Negotiating Your AI Job Offer: Equity, Bonuses & Perks Explained

Artificial intelligence (AI) has proven itself to be one of the most transformative forces in today’s business world. From smart chatbots in customer service to predictive analytics in finance, AI technologies are reshaping how organisations operate and innovate. As the demand for AI professionals grows, so does the complexity of compensation packages. If you’re a mid‑senior AI professional, you’ve likely seen job offers that include far more than just a base salary—think equity, bonuses, and a range of perks designed to entice you into joining or staying with a company. For many, the focus remains squarely on salary. While that’s understandable—after all, your monthly take‑home pay is what covers day-to-day expenses—limiting your negotiations to salary alone can leave considerable value on the table. From stock options in ambitious startups to sign‑on bonuses that ‘buy you out’ of your current contract, modern AI job offers often include elements that can significantly boost your long-term wealth and job satisfaction. This article aims to shed light on the full scope of AI compensation—specifically focusing on how equity, bonuses, and perks can enhance (or sometimes detract from) the overall value of your package. We’ll delve into how these elements work in practice, what to watch out for, and how to navigate the negotiation process effectively. Our goal is to provide mid‑senior AI professionals with the insights and tools to land a holistic compensation deal that accurately reflects their technical expertise, leadership potential, and strategic importance in this fast-moving field. Whether you’re eyeing a leadership role in machine learning at an established tech giant, or you’re considering a pioneering position at a disruptive AI startup, the knowledge in this guide will help you weigh the merits of base salary alongside the potential riches—and risks—of equity, bonuses, and other benefits. By the end, you’ll have a clearer sense of how to align your compensation with both your immediate lifestyle needs and long-term career aspirations.