Quantitative Research
We are a team of mathematicians, scientists and technologists working to predict the future.
Solving financial markets presents one of the most fascinating, complex, high-stakes quantitative problems in existence today.
Markets are always evolving. They change every nanosecond, everywhere around the globe, generating trillions of data points.
To navigate them successfully, we build ML models to anticipate moves and design trading algorithms. In a hyper-competitive space like trading, we are constantly innovating at speed to win.
Explore. Hypothesize. Test. Iterate.
As a quantitative researcher, your goal is to discover trading facts by pursuing truth and knowledge amidst a sea of data and noise. We follow the scientific process, bringing contributions together from all fields. Through comprehensive research, back testing and superb engineering implementation, we translate breakthroughs into live trading strategies at speed and at scale.
A place to reach your full potential
Nobody can reach their full potential alone. The Quantitative Research team is built upon collaboration and openness. Knowledge is shared. We build upon each other’s research. We are a flat organizational structure and have a global profit-sharing pool – rewarding great ideas that come from anywhere.
As a team we are here to win at the greatest quantitative competition in the world. We need to constantly improve to stay ahead and we look for individuals who take that mission personally – to make tomorrow better than today.
“At Optiver, my academic pursuits in applied mathematics and stochastic optimal control found a dynamic extension, allowing me to conduct research that sees tangible impacts in the real world. I’m surrounded by smart people in an environment that rewards well, in an open and collaborative culture that truly amplifies our collective and individual potential.”
Picture yourself here
- Most successful candidates possess a M.S. or Ph.D. in a STEM discipline.
- Practical experience in contemporary machine learning, large dataset management, and statistical model creation are highly advantageous.
- A track record of leadership in academia is preferred.
- Technical requirements include a strong mathematical base, excellent programming abilities, especially in Python, C/C++, or MatLab, and expertise in data science methodologies and time series analysis.
- In addition to technical and academic achievements, we appreciate candidates with outstanding communication abilities, a commitment to ongoing learning, and a competitive spirit.
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