Fundamental Equities Portfolio Manager - Machine Learning Platform

Kepler Fi is the independent, radical innovation arm of one of the worlds 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 Fundamental Equities PM to oversee a long-horizon, deep fundamental research, discretionary equities portfolio. The role will focus on leveraging a radically innovative machine learning driven investment platform to develop quantamental-like investing strategies in the construction and management of equities investments.

This role is a great opportunity for an experienced individual with an entrepreneurial mindset looking to leverage their leadership and quantitative skills to build out a new quantamental equity portfolio.


Responsibilities:

  • Design and develop an investment policy statement, objectives and procedures for a new machine learning based approach to long term fundamental investing
  • Work closely with technical expertise to identify key areas to apply machine learning in a discretionary investing process
  • Establish a process to critically assess potential equity investments, related processes, and risk management
  • Establish processes and guidelines for portfolio construction and management
  • Collaborate with external and internal constituents to ensure appropriate risk management, including direct interaction with investment committee
  • Run a book investing in equities utilizing machine learning in a bottom up approach

Must Haves:

  • 8-10 years of equities investment experience in either a fundamental discretionary fund or a quantamental fund
  • 4+ years of portfolio management experience
  • Basic level of programming technical skills or exposure to working with technical professionals
  • Compelling presentation skills, outstanding verbal and written communication skills
  • Proven track record of academic and professional achievement

Nice to Haves:

  • Experience with machine learning or large-scale data analysis techniques within the finance sector



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