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 two quantitative researchers to collaborate with for research on predictive modeling for intraday / short horizon futures and other macro predictive modeling.
You will take on the following responsibilities:
- Create predictive models from feature sets created both within the fast macro team and from outside the fast macro team
- Write profiling and pre-processing logic on features for predictive modeling
- Devise statistical tests to isolate apparently predictive power that is not monetizable or that comes from mechanical effects and microstructure biases, and improving error functions used in fitting against discovery of such noise
- Developing an understanding of monetization to reflect the ability to hedge into appropriate residualization used in fit targets
- Developing ready “off-the-shelf / pre-trained” learners and workflows to quickly estimate the value of new features or new sets of features, both directly and through conditioning existing feature sets
- Developing both linear and non-linear (e.g. trees, neutral network architectures) learner stacks, and workflows (sampling, batching, shuffling, etc.) around them
- Our team is tightly knit and open, and we constantly work in adjacent areas. Predictive modeling is even more closely tied to monetization at short horizons. Given interest, there will be opportunities to extend predictive modeling work both into features on the one side and monetization on the other.
You should possess the following qualifications:
- Have worked on predictive modeling using features which capture intraday variation in market pricing, especially microstructure and short-lookback technical indicators
- 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
- Have strong faculty with multiple learner stacks (e.g. linear, tree, neural network, etc.) to build predictive models from feature-sets ranging in size from one to ~10,000 features
- Have a good understanding of monetization constraints and how they are reflected into fit targets and the output characteristics of predictive model
- We have a preference for candidates who have done numerate graduate work, for example in probability and statistics, signal processing, computer science, machine learning, and other numerate engineering disciplines. We have a very strong preference for prior work with large datasets.
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 $325,000. 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.