Location
London, Greater London, England, United Kingdom

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 within GSET is responsible for the research, design, and continuous improvement of the firm's electronic execution algorithm platform. This platform enables institutional clients to access liquidity and execute orders efficiently across global markets.

As a member of the London-based Algo R&D team, you will work in a collaborative, intellectually rigorous environment that values innovation, scientific integrity, and real-world impact. You will leverage deep expertise in market microstructure, statistical modelling, and machine learning combined with world-class engineering to build and enhance algorithms that optimise execution quality, minimise market impact, and adapt intelligently to real-time market conditions.

Your work will span 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. You will collaborate closely with traders, technologists, sales teams, and clients to ensure our algorithms remain at the forefront of the industry.

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 in electronic trading, 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
- Intellectual curiosity combined with commercial pragmatism.
- First-principles thinker who understands and challenges model assumptions.
- Collaborative team player who thrives in interdisciplinary environments.
- Impact-oriented with a focus on measurable improvements in execution quality.
- Continuous learner who stays at the forefront of quantitative research.
- Contributor to an inclusive, high-performance team culture, willing to mentor and uphold 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.