Description

A Quantitative Portfolio Manager focused on Equity Stat Arb trading is looking for a ML Quant Researcher to join their team in San Francisco. The PM has 10+ years of experience building consistently profitable signals across US and Global Equity markets and is looking for someone who can successfully leverage non-linear methods to build trading signals that are orthogonal to their pre-existing core strategies.

This role is unique given the PM is open to strong talent coming from industry, tech or academia (PhD/Postdoc-level) as long as they have demonstrated the ability to tackle high-impact research in the machine learning space and have an innate interest in the technical projects that exist in quant finance. The researcher will be tasked with utilizing an array of technical + financial datasets and identify creative uses of nonlinear models to generate equity alpha signals to allocate capital towards.

While the team lead is open-minded on the sector/area this hire is coming from, qualified researchers must possess:

• 1-7 years of ML research experience (neural networks, regressions, reinforcement learning, probabilistic models)
• Expert statistical modeling skillset
• Strong experience working with large, messy datasets
• PhD in STEM field from ranked university
• Perfect communication skills
• Creative problem solver