Quantitative Data Engineer / Data Scientist

Key Responsibilities

Work with other front office equities and credit investment teams to define areas of research and relevant datasets to analyze and onboard.
Work with the Investment and Market Systems (IMS) and End-User Computing (EUC) teams on the development of, and our interface to, our enterprise data management platform.
Define and own equity-related data architecture that is not centrally managed by the IMS team.
Build and manage data pipelines to extract and cleanse raw data and archive into a high-performance production environment to be used for quantitative research.
Assist in the development of an internal quantitative research platform and associated APIs.
Write effective documentation and research reports communicating complex quantitative topics and relationships effectively to non-technical stakeholders.

Technical Competencies, Skills and Experience

Key Competencies:

Professional experience of statistical modelling of time series data, ideally financial and with a STEM degree with post-graduate research in the field.
Expert knowledge of data architecture and storage, ideally SQL Server, as well as advanced Python skills with experience of writing and deploying production-level code.
Have opinions on database best practice and associated technologies such as ORMs.
Experience supporting quantitative research tools such as backtesting engines, returns analysis frameworks, optimization tools and machine learning models.
Experience of Python data analysis libraries such as Pandas, NumPy, SciPy, Scikit-learn, etc.
Experienced writing technical documentation and using version control frameworks such as Git.

General Skills and Experience:

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