Location
London

Hours
Full Time

Salary
Negotiable

About the Role
Goldman Sachs Electronic Trading (GSET) operates at the intersection of technology, quantitative research, and global markets. The Algo R&D team designs and continuously improves the firm's electronic execution algorithms, enabling institutional clients to access liquidity and execute orders efficiently. This role involves working on the full lifecycle of algorithmic trading — from research on price formation and liquidity dynamics, through model development and back-testing, to production deployment and live performance monitoring.

As a member of the London-based Algo R&D team, you will collaborate with traders, technologists, sales teams, and clients to ensure our algorithms remain industry-leading. You will have access to comprehensive datasets, cutting-edge infrastructure, and a global network of experts to solve challenging problems in modern financial markets.

Key responsibilities include enhancing execution algorithms for cash equities, conducting rigorous quantitative research on market microstructure and transaction cost analysis, building and maintaining statistical and machine learning models, collaborating with technology teams to productionize research, performing back-testing and live A/B testing, analysing large-scale tick data, and partnering with sales and trading teams to translate client feedback into research priorities. You will also stay current with academic literature, regulatory changes, and the competitive landscape, and present research findings to senior stakeholders.

Experience
- Advanced degree (Master's or PhD) in a quantitative discipline such as Mathematics, Statistics, Physics, Computer Science, Financial Engineering, or related fields.
- 5+ years of experience in quantitative research related to execution or trading algorithms at a sell-side bank, buy-side firm, or proprietary trading firm.
- Deep understanding of market microstructure concepts including 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 such as tick data and order-book snapshots.
- Solid grasp of transaction cost analysis (TCA) methodologies and execution benchmarks.
- Excellent communication skills with the ability to convey complex quantitative concepts to both technical and non-technical audiences.

About You
- First-principles thinker who understands and challenges model assumptions.
- Collaborative team player who thrives in interdisciplinary environments.
- Impact-oriented, motivated by measurable improvements in execution quality.
- Continuous learner who stays at the forefront of quantitative research.
- Culture carrier who contributes to an inclusive, high-performance team and upholds high ethical standards.

Qualifications
- Preferred experience with equities execution algorithms in European or global markets.
- Understanding of regulatory frameworks relevant to algorithmic trading such as MiFID II.
- Strong programming skills in Python.
- Ability to query data using kdb+/q.
- Familiarity with reinforcement learning or deep learning techniques applied to optimal execution problems.