Quant jobs / Goldman Sachs

Quantitative Researcher – Quantitative Execution Services

A research role on the quantitative execution services team, which designs and tunes algorithmic trading strategies. You work with algo developers, strats and traders to take ideas into production.

What you’d work on

Our reading of the posting: what a new hire in this role would most likely spend their first months on.

Build internalisation logic that lowers market impact
Create and refine trading signals from historical and real-time data
Maintain backtesting frameworks and run robustness simulations
Watch live strategy performance and find improvements
Hand over production-ready research to algo developers

What they ask for

Required

Python, C++ or Java
Linear algebra, probability and statistics
Data pipelines
Debugging and performance optimisation
Electronic trading, research or strategy experience
Communication

Preferred

Slang and KDB+
1+ years in financial services
Combining alpha research with execution research
Market microstructure and execution algorithms
Data science and machine learning

Education: Not stated
Experience: Around 1 year in financial services preferred
Languages and tools: Python
C++
Java
Slang
KDB+/q
Markets: equities

How to prepare

Build an execution simulator that measures impact of different order slicing schedules
Backtest a short-horizon signal on order book data with realistic fills
Learn the basics of kdb+/q for tick data
Study market microstructure papers on impact and internalisation

What to study

The HFT Quantitative Researcher roadmap sets out the stages for this kind of role, with free resources for each.

Open the roadmap

Apply

Read the full posting on Goldman Sachs’s site before applying: requirements, location and pay can change after we check them.

View the posting on Goldman Sachs’s site ↗

SelfMadeQuant wrote this summary from the employer’s public posting, last checked 6 November 2026. SelfMadeQuant isn’t affiliated with Goldman Sachs and doesn’t handle applications.