Global Banking & Markets - GSET - Quantitative Strategist - London - VP

What We Do

Goldman Sachs Electronic Trading (GSET)sits at the intersection of technology, quantitative research, and global markets.

We design and operate the firm's suite ofelectronic execution algorithmsthat enable institutional clients to access liquidity and execute orders efficiently.

Within GSET, theAlgo R&Dteam is responsible for theresearch, design, and continuous improvementof our execution algorithm platform.

We combine deep expertise inmarket microstructure, statistical modelling, and machine learningwith world-class engineering to build algorithms that optimise execution quality, minimise market impact, and adapt intelligently to real-time market conditions.

Our work spans the full lifecycle of algorithmic trading - from researchinto price formation and liquidity dynamics, throughmodel development and back-testing, toproduction deployment and live performance monitoring.

We partner closely with traders, technologists, sales teams, and clients to ensure our algorithms remain at the forefront of the industry.

As a member of the London-based Algo R&D team, you will join acollaborative, intellectually rigorous groupthat values innovation, scientific integrity, and real-world impact. You will have access to one of the most comprehensive datasets in the industry, cutting-edge infrastructure, and a global network of experts - all in service of solving some of the most challenging problems in modern financial markets.

Who We Look For

We seek individuals who combineintellectual curiosity with commercial pragmatism- people who are as excited about solving a hard research problem as they are about seeing their work drive measurable improvements in execution quality for our clients:

• First-principles thinkers- You don't just apply off-the-shelf models; you deeply understand the assumptions behind them and know when to challenge or adapt them to the realities of live markets.
• Collaborative partners- You thrive in a team environment where ideas are debated openly. You enjoy working across disciplines - with technologists, traders, salespeople, and clients - and can tailor your communication to each audience.
• Impact-oriented- You measure success not just by the elegance of your models but by their impact on execution quality. You are motivated by outcomes that matter to the business and our clients.
• Continuous learners- You stay at the frontier of quantitative research, whether that means reading the latest papers on optimal execution, experimenting with new ML techniques, or learning from post-trade analytics.
• Culture carriers- You contribute to an inclusive, high-performance team culture. You are willing tomentor others, share knowledge, and uphold the highest ethical standards in everything you do.

Responsibilities

• Enhanceexecution algorithms(e.g., VWAP, Participate, adaptive/liquidity-seeking strategies) for cash equities.
• Conduct rigorousquantitative researchon market microstructure, order-book dynamics, venue analysis, and transaction cost analysis (TCA).
• Build and maintainstatistical and machine learning modelsfor short-term price prediction, fill-rate estimation, market-impact modelling, and optimal order placement/scheduling.
• Collaborate with technology teams toproductionize researchinto low-latency, high-reliability trading systems.
• Performback-testing, simulation, and live A/B testingof algorithm enhancements; define and track performance metrics.
• Analyselarge-scale tick datato identify alpha opportunities and areas for algo improvement.
• Partner with sales, trading, and client-facing teams to translateclient feedbackand business requirements into research priorities.
• Stay current withacademic literature, regulatory changes (e.g., MiFID II best-execution obligations), and competitive landscape in electronic trading.
• Present research findings and strategic recommendations tosenior stakeholdersand cross-functional partners.

Basic Qualifications

• Advanced degree(Master's or PhD) in a quantitative discipline - Mathematics, Statistics, Physics, Computer Science, Financial Engineering, or a related field.
• 5+ yearsof experience in quantitative research related to execution/trading algorithms at a sell-side bank, buy-side firm, or proprietary trading firm.
• Deep understanding ofmarket microstructureconcepts: order types, venue fragmentation, latency, queue priority, and market-impact models.
• Proven experience withstatistical modelling, time-series analysis, and/ormachine learningapplied to financial data.
• Proficiency in working withlarge datasets(tick data, order-book snapshots).
• Solid grasp oftransaction cost analysis (TCA)methodologies and execution benchmarks.
• Excellentcommunication skills- ability to convey complex quantitative concepts to both technical and non-technical audiences.

Preferred Qualifications

• Experience withequities execution algosin European or global markets.
• Understanding ofregulatory frameworksrelevant to algorithmic trading (MiFID II).
• Strong programming skills inPython.
• Ability to query data in kdb+/q.
• Familiarity withreinforcement learningordeep learningtechniques applied to optimal execution problems.

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

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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.