Job description

Millennium is looking for an extremely talented Data Scientist to join its equities investment group in London. The successful candidate will combine strong technical skills, financial insights and statistical techniques to explore, analyze, and harness a large variety of structured and unstructured datasets, in order to build data-centric solutions which give teams an edge in their investment process. The candidate must possess the communications skills needed to not only explain sophisticated statistical and technical techniques in clear simple language, but to also listen and understand problems as explained by investment teams in their vernacular.


Principal Responsibilities


  • Analyze, manage, and store vast amounts of datasets from market to alternative data vendors
  • Communicate and provide data insights to investment teams (portfolio managers and analysts)
  • Mine new alternative datasets to uncover signals and determine value
  • Write code to tag, clean, and normalize large unstructured and structured datasets
  • Back test datasets to assess their correlation to company KPIs
  • Communicate and provide data insights to investment teams (portfolio managers and analysts)
  • Work alongside the data sourcing team to identify new potential data products and vendors
  • Develop data analysis tooling and libraries alongside the rest of the data science team
  • Construct visualization dashboards to present key insights


Qualifications/Skills Required


  • BS, MS, or PhD degree in Statistics, Computer Science, Applied Mathematics, or related field.
  • At least 3+ years of working experience within financial data science
  • At least 3+ years of programming experience in Python
  • React and experience with the Django framework a plus
  • Excellent communicator
  • Highly proficient in writing advanced and optimized SQL queries
  • Hands on experience with ETL processes and API construction
  • Experience in company fundamental financial metrics (e.g. revenue, cash flow, margin, etc.)

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