Artificial intelligence (AI) and machine learning (ML) have collectively made a significant impact on hedge funds — and on the modern investing industry at large. Cognitive technologies have strengthened a variety of industry processes, changing how we approach popular business models and strategize to achieve success.
Subsequently, AI/ML’s growing corporate presence has created a new battleground for companies across many industries; it is now both a top spending priority and a significant consideration in aspects of the hiring process. In hedge funds, the latter has become especially prevalent as funds — both traditional and progressive — consider new candidates with an efficient quantitative skill set.
New approaches to hiring
Increased awareness of AI/ML technology’s industry-specific benefits, paired with continued emphasis on the quantamental hedge fund model, has reshaped expectations and demands expressed in hedge fund hiring cycles. Many traditional funds are now considering the addition of a “quantitative pillar” of sorts, utilizing young minds to evolve with the times and keep a foot in AI/ML-based industry changes.
This notion in mind, we are now seeing a number of firms embracing candidates from the technology industry, who may not know the complex ins and outs of finance but have worked with a number of emerging technological concepts. In this case, candidates are weighed based on their experience with an external spectrum of skills — as well as their willingness to glean a working knowledge of finance in general. In many cases, this approach has led to the streamlining of current hedge fund operations.
For example, candidates possessing a strong knowledge of Natural Language Processing (NLP) have risen as a hiring consideration for funds specializing in Equity Long Short investing, as NLP technology has become an asset for both information retrieval and the classification of company financial statement content. These candidates are sought to inject NLP into the reading and analysis of company reports and statements, the collection of up-to-date information on stocks, and the overall efficiency of equity research — this allows funds to provide timely updates on key matters. Other hirees may be considered for their experience with a broad range of data sets, applying this knowledge to fund-specific data entry and management.
As AI/ML technology continues to broaden and redefine our expectations for modern investing, this style of hiring is poised to become more widespread in the foreseeable future.