Kepler Fi is the independent, radical innovation arm of one of the world's largest sovereign wealth funds, focused on moonshots within institutional asset management. We perform strategic investments, joint ventures, and internal development of ambitious ideas to transform the asset management space.

We are looking for a Machine Learning (ML) Research with software engineering exposures to provide technical support of a radically innovative machine learning driven investment platform. The goal is to develop strategies mimicking Fundamental investors’ mindset and approaches. 

This role is a great opportunity for an experienced individual to leverage their quantitative and machine learning skills to build out new strategies based on Artificial Intelligence inspired by fundamental heuristics and investment approach.

Responsibilities:

  • The Researcher will focus on the integration, analysis and modeling of quantitative and fundamental data in the production of investment recommendations 
  • Build data ETL pipelines, robust machine learning systems to support the quantification of Fundamental investment processes
  • Apply ML techniques for complex output problems to forecast financial statements’ line items
  • Formulate the challenge problems in the long-term fundamental investing approaches and adapt ML techniques to solve them

Must Haves:

  • 5-7+ years of quantitative research experience (Data Science, etc)
  • Extensive experiences with exploratory data analysis, statistical analysis and testing, and model development
  • Deep knowledge of underlying mathematical foundations of statistics, machine learning, optimization, economics, and analytics
  • Ability to use a language like Python to work efficiently at scale with large data sets

Nice to Haves:

  • Experience in algorithm development and prototyping
  • Experience with causal ML methods is a plus
  • Buy-Side quant research experience
  • Exposure to large institutional portfolios
  • Strong verbal and written communication skills in order to present findings to larger team

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