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 firms 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 dont 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 data to 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(Masters 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.