Pantera Capital is hiring a Quantitative Researcher for our San Francisco office to focus primarily on supporting quantitative analyses and strategies of the firm’s multiple hedge fund portfolios. As an Associate of the investment team, you will leverage exclusive proprietary data and resources in computational modeling to create cutting edge investment insight with one of the oldest and largest investment firms at the forefront of an unexplored and burgeoning new asset class. The right candidate would be able to start immediately.
The primary responsibilities of the Quantitative Researcher can be broken down into three areas:
- Source, evaluate, and productionize alternative datasets with potentially leading latent information against time series objectives.
- Extract, encode, transform, and/or reduce for datasets that support multivariate factor modeling.
- Assemble workflows based on toolsets in Python (Pandas, NumPy, SciPy, etc), MySQL, machine learning (TensorFlow, etc), accelerated matrix transformation (Dask, Eigen, Blaze, etc) for multivariate modeling of market dynamics.
- Evaluate model effectiveness against benchmarks in terms of both statistical power and financial risk-adjusted performance as an investment signal to live trading strategies.
Analysis and Modeling
- Support the investment committee in presenting periodic reports and visualizations of portfolio performance and other discretionary research objectives of the broader market.
The ideal candidate will have the following qualifications:
- Internship or 2-4 years relevant work experience in trading, quantitative investment, venture capital, investment banking, management consulting, and/or data science/engineering.
- Proficiency in Python 3, C++, MySQL 5.6+.
- High degree of communication skills, analytical intuition, directed curiosity, multitasking ability, and attention to detail.
- High ability to work independently and conceptualize abstractly articulated goals into quantitatively provable objectives for self-guided execution.
- Advanced coursework in – applied mathematics, Bayesian optimization, stochastic modeling, statistics, computer science, and algorithms.
- Project experience in two or more of – machine learning, time-series forecasting, pattern classification, and applied data analysis.
- Strong interpersonal skills in working with a multidisciplinary team and able to describe both the methodologies and conclusions of potentially sophisticated technical concepts at both expert and non-technical levels.