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Quantitative Developer - Commodities

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Company
Jobleads-US
Job location
New York, United States
Salary
Undisclosed
Posted
Hosted by
Appcast

Job details

Quantitative Developer - Commodities

Millennium is a top tier global hedge fund with a strong commitment to leveraging innovations in technology and data science to solve complex problems for the business. We are assembling a strong Quant Technology team to build our next generation in-house analytics and trader support tools. This team will sit under the Fixed Income & Commodities Technology (FICT) group and will develop and maintain the in-house pricing libraries to support trading in Fixed Income, Commodities, Credit, and FX business at Millennium. FICT provides a dynamic and fast-paced environment with excellent growth opportunities.
Primary Responsibilities
  • Work with Quant Researchers, Portfolio Managers & Technology teams to build commodities fundamental analytics and modelling platform from scratch
  • Develop scalable libraries and APIs for commodities fundamental modelling across multiple assets and geographies
  • Build, help maintain and operate end to end modelling pipeline involving diverse and large set of statistical and machine learning models
  • Build scalable tools to aid data analysis and visualization
  • Work with broader team to develop robust delivery and operating models, enabling rapid development and scalable deployment of new capabilities
Required Skillset
  • Strong Python Fundamentals knowledge (for data science) and SQL/ Database experience
  • Solid experience with all stages of data modelling pipeline – Data access, Transformation, model training and inference pipeline and Data analysis tools
  • Able to work independently with hands on experience on complete software development lifecycle and relevant development tools
  • Prior experience as Quant Developer/ Data Scientist in financial industry
  • Commodities and hands on statistical/ data modelling experience preferred
  • Familiarity with tools and libraries like Airflow, Flask, Dash etc preferred
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