Principal Data Scientist2024-04-16T11:28:21+01:00
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Principal Data Scientist Job Vacancy in London, England, UK
IHS Markit
Position Summary
IHS Markit is a global market leader in providing information, analytics and solutions for industries and markets that drive economies worldwide. The Financial Services division is the largest division within IHS Markit.
This is an opportunity to join the Advanced Analytics Team where, as a senior member, you can directly contribute to the value we deliver to our clients in Financial Services.
At IHS Markit, we have extraordinary and unique data spanning Financial Services, Energy, Economic Country Risk, Automotive, Maritime & Trade and other sectors.
If, like us, you are excited by what can be done to improve our products and services by intelligently combining this data, then you could be a great addition to the team.
Responsibilities
Develop models, algorithms and data pipelines that leverage wealth of IHS Markit data to provide actionable insights to clients
Lead data science product development end to end: from POC to productization
Actively explore and identify the latest relevant techniques
Identify opportunities for innovation across Financial Services
Requirements and Skills
5+ years of professional experience in Advanced Analytics / Data Science / Machine Learning / Quant, including in the Financial Services sector
Hands on project lifecycle experience, from business analysis to productization
Coding experience (preferably Python) to write robust and high standard code; experience with version control (preferably Git)
Experience working with databases (e.g. SQL)
Ability to quickly acquire new technical skills
Experience of working with large data sets and distributed computing
Good understanding of mathematical foundations of Machine Learning models
Able to translate business problems into problems that can be solved with Data Science
Desirable Skills
Econometrics / Financial Engineering background
Time series modelling
Cloud (AWS, GCP, Azure)
Experience with model visualization and explainability
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