Best suited to candidates with financial market experience and technical skills in Python and SQL, working in a collaborative, data-driven investment management environment in London.
Derived from job-description analysis by Serendipath's career intelligence engine.
Original posting from Qube Research & Technologies via Greenhouse
Qube Research & Technologies (QRT) is a global quantitative and systematic investment manager, operating in all liquid asset classes across the world. We are a technology and data driven group implementing a scientific approach to investing. Combining data, research, technology, and trading expertise has shaped QRT’s collaborative mindset which enables us to solve the most complex challenges. QRT’s culture of innovation continuously drives our ambition to deliver high quality returns for our investors.
You will work at the intersection of financial markets, data science and engineering, supporting the discovery, evaluation and integration of financial datasets used across quantitative research and trading. The role combines technical expertise with an understanding of Macro markets, working closely with Quantitative Researchers, Portfolio Managers and Traders to translate business requirements into practical data solutions. You will engage directly with external data providers, assess existing data assets and identify opportunities to expand the firm's data capabilities.
Your future role within QRT
Collaborate with Quantitative Researchers, Portfolio Managers and Traders to understand their data requirements and identify suitable datasets for research and trading strategies
Evaluate existing data assets and external vendor offerings, assessing coverage, quality, consistency and suitability for specific financial applications
Manage the discovery, evaluation and onboarding of new datasets, with a focus on Macro markets, including Rates, bonds, swaps, derivatives and alternative data
Engage directly with external data providers to understand their offerings, assess APIs and investigate data availability, structure and delivery mechanisms
Develop Python tools and data pipelines to extract, clean, normalise and analyse financial datasets from multiple sources
Investigate discrepancies across data providers, including differences in instrument identifiers, pricing conventions, timestamps and reference data
Maintain and improve the quality and consistency of existing datasets, proactively resolving issues and identifying opportunities to enhance data coverage
Collaborate with engineering teams to automate data ingestion, validation and integration processes, ensuring reliable delivery into research and trading environments
Your present skillset
Minimum 2 years of relevant professional experience in Data Science, Data Analysis, Data Engineering or a similar role within financial markets
Practical understanding of financial markets and instruments, with exposure to Macro, Fixed Income, Rates, Credit or related asset classes
Strong Python programming skills, including experience with data analysis libraries such as Pandas and NumPy
Good SQL skills and experience working with structured financial datasets
Experience sourcing, analysing and processing financial data, with an understanding of data quality, validation and consistency across multiple sources
Ability to understand financial concepts and communicate effectively with Quantitative Researchers, Portfolio Managers, Traders and external data vendors
Strong analytical and problem solving skills, with the ability to investigate unfamiliar datasets and identify appropriate solutions independently
Familiarity with external data providers, financial data platforms and API integration
Ability to manage competing priorities, take ownership of data initiatives and collaborate effectively across technical and business functions
Degree in a quantitative discipline such as Mathematics, Statistics, Computer Science, Physics, Engineering or Finance; a postgraduate qualification is beneficial
Experience developing data pipelines, automating data workflows or supporting production data infrastructure is beneficial
Knowledge of financial data concepts such as yield curves, instrument reference data, market data and pricing conventions is advantageous
QRT is an equal opportunity employer. We welcome diversity as essential to our success. QRT empowers employees to work openly and respectfully to achieve collective success. In addition to professional achievement, we are offering initiatives and programs to enable employees achieve a healthy work-life balance.