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 of electronic execution algorithms that enable institutional clients to access liquidity and execute orders efficiently.

Within GSET, the Algo R&D team is responsible for the research, design, and continuous improvement of our execution algorithm platform. 

We combine deep expertise in market microstructure, statistical modelling, and machine learning with 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 research into price formation and liquidity dynamics, through model development and back-testing, to production 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 a collaborative, intellectually rigorous group that 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 combine intellectual 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 to mentor others, share knowledge, and uphold the highest ethical standards in everything you do.

Responsibilities

• Enhance execution algorithms (e.g., VWAP, Participate, adaptive/liquidity-seeking strategies) for cash equities.
• Conduct rigorous quantitative research on market microstructure, order-book dynamics, venue analysis, and transaction cost analysis (TCA).
• Build and maintain statistical and machine learning models for short-term price prediction, fill-rate estimation, market-impact modelling, and optimal order placement/scheduling.
• Collaborate with technology teams to productionize research into low-latency, high-reliability trading systems.
• Perform back-testing, simulation, and live A/B testing of algorithm enhancements; define and track performance metrics.
• Analyse large-scale tick data to identify alpha opportunities and areas for algo improvement.
• Partner with sales, trading, and client-facing teams to translate client feedback and business requirements into research priorities.
• Stay current with academic literature, regulatory changes (e.g., MiFID II best-execution obligations), and competitive landscape in electronic trading.
• Present research findings and strategic recommendations to senior stakeholders and 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+ years of experience in quantitative research related to execution/trading algorithms at a sell-side bank, buy-side firm, or proprietary trading firm.
• Deep understanding of market microstructure concepts: order types, venue fragmentation, latency, queue priority, and market-impact models.
• Proven experience with statistical modelling, time-series analysis, and/or machine learning applied to financial data.
• Proficiency in working with large datasets (tick data, order-book snapshots).
• Solid grasp of transaction cost analysis (TCA) methodologies and execution benchmarks.
• Excellent communication skills — ability to convey complex quantitative concepts to both technical and non-technical audiences.

Preferred Qualifications

• Experience with equities execution algos in European or global markets.
• Understanding of regulatory frameworks relevant to algorithmic trading (MiFID II).
• Strong programming skills in Python.
• Ability to query data in kdb+/q.
• Familiarity with reinforcement learning or deep learning techniques applied to optimal execution problems.