Research Associate - Machine Learning Product

Kepler is a radical innovation lab working in the world of investment management. We build and invest in radical innovations with the potential to disrupt institutional asset management. As an autonomous division of GIC, one of the world’s largest sovereign wealth funds, our group is nimble, and innovative in spirit, but with the backing to tackle monumental projects and have enormous impact.

We are looking for a Research Associate to help develop a new approach to strategy replication leveraging machine learning. You will be identifying and analyzing equity investment strategies, alternative data sources, and NLP to develop inputs into state-of-the-art algorithms.

You’re you looking to combine a passion for equities with data-driven decision making. Always willing to roll up your sleeves in order to find results, you thrive in a collaborative environment where you’re always being challenged to find solutions to unique problems.

Job description/Role Responsibility:

  • Working with sell-side/buy-side analysts to identify successful investment strategies appropriate for replication
  • Working with data analysts and machine-learning engineers to drive the creation, direction, and strategy of a long-term fundamental investing product
  • Developing proprietary data sources and analyzing alternative data sources
  • Managing external partners involved in data set creation (e.g. Crowdsourcing vendors, etc.)
  • Working with team leaders to identify and translate key findings into presentation materials for internal and external stakeholders 

Must Have’s
  • 2-4 years experience in a data analytics / research related role on the buy-side or sell-side 
  • Experience in public equities
  • Quick learner, comfortable operating in an unstructured environment
  • Strong understanding of fundamental investing
  • Superior communication skills
  • Experience working in a highly collaborative environment

Nice to Have’s
  • Ability to manipulate data within SQL
  • Strong familiarity with data requirements for robust statistical analysis
  • Educational background in economics, finance or related field 
  • Interest in or exposure to machine learning
  • Familiarity with crowdsourcing concepts

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