Finance AI & Quant (FAIQ)
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Assessing your cultural and operational fit
AI quant/researcher with a background in theoretical physics with an extensive experience in building cutting-edge statistical and advanced machine learning algorithms to solve practical problems in finance, especially within portfolio modeling, forecasting models, and optimal control models including reinforcement learning and inverse reinforcement learning. In 2022, Risk.net named Igor the Buy-Side Quant of the Year.
Peter the Great St.Petersburg Polytechnic University
M.Sc., experimental nuclear physics
N/A – Present
Petersburg Nuclear Physics Institute, RAS
Ph.D. program, theoretical physics
N/A – Present
Tel Aviv University
Ph.D., Theoretical High Energy Physics
N/A – Present
Fidelity Investments
AI Asset Management
January 1, 2019 – Present
NYU Tandon School of Engineering
Research Professor of Financial Machine Learning
September 1, 2017 – August 1, 2020
New York City Metropolitan Area
Invisible Hand Computing LLC
Principal Owner
April 1, 2017 – April 1, 2021
JPMorgan Chase
Executive Director, Quantitative Research
June 1, 2003 – March 1, 2017
Bloomberg LP
Quantitative Developer
June 1, 1999 – June 1, 2003
The University of British Columbia
Postdoctoral researcher
January 1, 1996 – January 1, 1999
Vancouver, British Columbia
Cultural Fit Analysis
The candidate's background is heavily skewed towards quantitative finance and theoretical physics. While this demonstrates strong analytical rigor, the target role of 'AI Project Management Intern' suggests a need for broader project management skills, potentially in diverse AI applications beyond finance, and an intern-level role might not align with their extensive senior-level experience. The lack of diverse project experience outside of finance and theoretical physics might indicate a narrow cultural fit for a general AI project management role, especially at an intern level.
Soft Skills & Operational Fit
The candidate's extensive experience in research and development roles, including teaching, suggests strong analytical thinking, problem-solving, and communication skills. Their background in leading quantitative research teams implies leadership and project management capabilities. However, specific details on collaboration, stress handling, and work attitude are not available from the provided data.