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AI Summary
We are looking for an intellectually curious Data Scientist to join our research team on a full-time, permanent basis. You will own the design and operation of data pipelines that collect, clean, model, and evaluate alternative data sources with relevance to equity markets. The work sits at the intersection of statistical analysis, financial research, and data engineering. Expect equal parts building robust pipelines, applying rigorous quantitative methods, and communicating findings clearly to stakeholders across the business.
What You'll Do
Design, build, and maintain web scrapers to collect structured and unstructured data from public sources.
Clean, normalise, and validate incoming datasets; flag anomalies and document methodology.
Evaluate data quality and relevance by assessing coverage, timeliness, and signal potential against equity market use cases.
Conduct desk research on companies, sectors, and market themes to provide context for quantitative findings.
Produce clear, concise written and visual outputs that communicate data findings and statistical conclusions to both technical and non-technical audiences.
Partner with strategic managers and senior researchers to iterate on data strategies, prioritise new sources, and translate quantitative findings into actionable insights.
What We're Looking For
Must Have
Proficiency in Python, particularly writing clean, well-structured scripts independently.
Hands-on experience with pandas for data manipulation, transformation, and analysis; this is a core part of day-to-day work.
Strong analytical mindset: you ask good questions of data and don’t take outputs at face value.
Attention to detail and a methodical approach to quality-checking your own work.
Statistical proficiency: comfortable with hypothesis testing, regression, distributions, and interpreting model outputs with appropriate rigour.
Proven ability to present data findings to non-technical audiences: translating complex analysis into clear narratives and actionable recommendations for business stakeholders.
Clear written and verbal communication, with the confidence to present findings independently.
Nice to Have
Prior exposure to equity markets, whether through professional experience, personal investing, or independent research.
Exposure to AWS cloud services, particularly S3 for data storage or Athena for querying large datasets.
Familiarity with SQL or basic database querying.
Exposure to financial data providers (Bloomberg, Refinitiv, etc.) or alternative data concepts.
Experience with version control (Git) and working in a collaborative codebase.
About You
You are a practising data scientist with demonstrable experience applying statistical methods and building data pipelines in a professional setting. A degree in data science, statistics, mathematics, economics, or a related quantitative field is preferred, but we are open to all backgrounds provided you have the skills and the drive to keep learning. If you have a genuine curiosity about markets and a talent for turning messy data into actionable insights, we want to hear from you.
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