Quantitative Data Developer | Python | Leading Systematic Hedge Fund | London

Stanford Black Limited; City Of London; On-site; GBP 90,000 - 130,000; Full time

Job summary

Stanford Black Limited is seeking an exceptional Python Engineer to work across data engineering, quantitative research and financial markets. You will own datasets and build infrastructure and processes to transform complex financial data into reliable, research-ready data.

You will collaborate with Quant Researchers and Traders on data-driven opportunities across global markets. You will develop sophisticated Python pipelines, explore alternative datasets and optimize time-series processing

Qualifications

• Excellent Python engineering skills with a focus on performance.
• Experience manipulating complex datasets using Pandas, Polars or NumPy.
• Strong SQL skills and experience with relational databases.
• Experience with financial data, quantitative research or systematic trading would be valuable.

Responsibilities

• Engineering sophisticated Python pipelines to process complex financial datasets.
• Working directly with Quant Researchers and Traders to unlock data-driven opportunities.
• Building high-performance data processing workflows using Python, Polars, Pandas and NumPy.
• Exploring alternative datasets to uncover valuable information.
• Solving problems involving time-series data, historical accuracy and data integrity.
• Optimising data extraction, transformation and validation for performance and scalability.
• Taking full ownership of new datasets from investigation to production.

Skills

Python

Time-series data handling

Performance optimization

Tools

Pandas

Polars

NumPy

SQL

Job description

One of the world's most sophisticated quantitative investment firms is seeking an exceptional Python Engineer to work across of data engineering, quantitative research and financial markets.

This is an opportunity to solve some of the most intellectually challenging data problems in finance, working directly with Quantitative Researchers and Traders whose investment decisions depend on the quality, accuracy and availability of your work.

You'll take ownership of datasets, building the infrastructure and processes that transform complex financial data into reliable, research-ready datasets.

Your work will directly support the discovery, development and evaluation of quantitative trading strategies across global markets.

What you'll be doing

• Engineering sophisticated Python pipelines to process complex financial datasets
• Working directly with Quant Researchers and Traders to unlock new data-driven trading opportunities
• Building high-performance data processing workflows using Python, Polars, Pandas and NumPy
• Exploring alternative datasets to uncover valuable, previously inaccessible information
• Solving challenging problems involving time-series data, historical accuracy and data integrity
• Optimising data extraction, transformation and validation processes for performance and scalability
• Taking full ownership of new datasets, from initial investigation through to production

You bring

• Excellent Python engineering skills with a strong understanding of performance and efficiency
• Experience manipulating complex datasets using Pandas, Polars or NumPy
• Strong SQL skills and experience working with relational databases
• Experience with financial data, quantitative research or systematic trading would be highly valuable.