The Voleon Group
Ashish Dhiman is a data scientist with diverse experience in analytics and risk management. Currently employed at The Voleon Group since February 2024, Ashish focuses on developing systematic Forex strategies with an emphasis on backtest improvements and alternative data features. Previously, as a Graduate Teaching Assistant at Georgia Institute of Technology from January 2023 to December 2023, Ashish assisted in teaching advanced data analytics courses. Ashish interned at Citi as a Quantitative Risk Analyst, applying NLP techniques to enhance e-trading risk controls, and at American Express in various data science roles, contributing to credit risk modeling and winning multiple awards for significant projects. Ashish holds a Master’s degree in Analytics and Data Science from Georgia Institute of Technology and a Bachelor’s degree in Aerospace Engineering from the Indian Institute of Technology, Kharagpur.
The Voleon Group
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Founded in 2007 by two machine learning scientists, The Voleon Group is a quantitative hedge fund headquartered in Berkeley, CA. We are committed to solving large-scale financial prediction problems with statistical machine learning. The Voleon Group combines an academic research culture with an emphasis on scalable architectures to deliver technology at the forefront of investment management. Many of our employees hold doctorates in statistics, computer science, and mathematics, among other quantitative disciplines. Voleon's CEO holds a Ph.D. in Computer Science from Stanford and previously founded and led a successful technology startup. Our Chief Investment Officer and Head of Research is Statistics faculty at UC Berkeley, where he earned his Ph.D. Voleon prides itself on cultivating an office environment that fosters creativity, collaboration, and open thinking. We are committed to excellence in all aspects of our research and operations, while maintaining a culture of intellectual curiosity and flexibility. The Voleon Group is an Equal Opportunity employer. Applicants are considered without regard to race, color, religion, creed, national origin, age, sex, gender, marital status, sexual orientation and identity, genetic information, veteran status, citizenship, or any other factors prohibited by local, state, or federal law.