Quantitative Researcher, Fast Monetization
New York, New York, United States
Two Sigma is a financial sciences company, combining data analysis, invention, and rigorous inquiry to help solve the toughest challenges in investment management, insurance technology, securities, private equity, and venture capital.
Our team of scientists, technologists, and academics looks beyond the traditional to develop creative solutions to some of the world’s most complex economic problems.
Our Fast Forecasting & Trading team is looking for a quantitative researcher to collaborate with research on intraday / short horizon futures monetization.
You will take on the following responsibilities:
- Take on primary responsibility for monetization research in a team that organizes work into three groups: (a) features, (b) predictive modeling and (c) monetization, and focuses especially on macro markets as above
- Work on design and iterative improvement of the decision logic that the actions of our monetization engine come out of - for example through utility function design or rules-based logic and calibration of the decision logic parameters with historical simulations and eventually post-trade data
- Reflect the limitations of TS infrastructure in our monetization design to produce estimates that are unbiased vs. our live realization. Work with partners in Engineering and other organizations to specify improvements that produce the most benefit.
- Reflect monetization reality in fit targets / responses being used for predictive research. Initiate research on liquidity events / triggers on which our decision logic fires.
- Our team is tightly knit and open, and we constantly work in adjacent areas, so if there is interest, work into feature creation and predictive modeling, especially with a monetization eye, is also always welcome.
You should possess the following qualifications:
- Have worked on monetization of signals that range in the horizon over which they have predictive power from a few minutes to about a day
- Have a strong understanding of the microstructure of the major futures exchanges, including CME, ICE and Eurex; tested through application to create features for prediction and for conditioning trading
- Can research and implement decision logic in various quantitative frameworks, ranging from utility function optimization to rules-based trading
- Have deployed latency-sensitive strategies in futures and other macro markets including FX, Treasuries, macro ETFs, etc., possibly including crypto assets
- We have a preference for candidates who have done numerate graduate work, for example in operations research, probability and statistics, signal processing, computer science, machine learning, or other numerate engineering disciplines and hard sciences.
You will enjoy the following benefits:
- Core Benefits: Fully paid medical and dental insurance premiums for employees and dependents, competitive 401k match, employer-paid life & disability insurance
- Perks: Onsite gyms with laundry service, wellness activities, casual dress, snacks, game rooms
- Learning: Tuition reimbursement, conference and training sponsorship
- Time Off: Generous vacation and unlimited sick days, competitive paid caregiver leaves
- Hybrid Work Policy: Flexible in-office days with budget for home office setup
The base pay for this role will be between $165,000 and $300,000. This role may also be eligible for other forms of compensation and benefits, such as a discretionary bonus, health, dental and other wellness plans and 401(k) contributions. Discretionary bonus can be a significant portion of total compensation. Actual compensation for successful candidates will be carefully determined based on a number of factors, including their skills, qualifications and experience.
We are proud to be an equal opportunity workplace. We do not discriminate based upon race, religion, color, national origin, sex, sexual orientation, gender identity/expression, age, status as a protected veteran, status as an individual with a disability, or any other applicable legally protected characteristics.