Quantitative Researcher - Scientific Discretionary

Job Description

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. We are looking for a quantitative researcher with an excellent background in quantitative research and machine learning to join our team. You should have prior experience applying quantitative method and machine learning technique in research in financial market. A deep experience with a specific asset class or geography is a plus, but flexibility and breadth of knowledge is equally important. In this role you will conduct quantitative research with Machine Learning and AI Models. You will research, analyze, develop, and implement financial investment strategies and ideas based on financial data. You will create and test complex quantitative-based financial investment ideas. You will utilize financial analysis, mathematical/statistical analysis, and predictive quantitative financial modeling/financial engineering skills to research, analyze, develop, and execute on data-driven solutions to financial investment problems. You will take on the following responsibilities:

• Research, identify, analyze, and assess specific potential new financial investment business opportunities across new asset classes and regions using systematic approaches and quantitative, mathematics/statistics-based machine learning methods.
• Share insights from results of quantitative research efforts focused on statistics, machine learning, and data science.
• Apply machine learning and AI models to research on complex unstructured data set.
• Collaborate in a team environment with portfolio manager, traders, researchers, and engineers.

You should possess the following qualifications:

• Master’s degree in computer science, Financial Engineering, Mathematics, or other relevant field plus 5-8 years of experience in building quantitative financial models and global macro market; or PhD degree in Computer Science, Financial Engineering, Mathematics or other relevant field plus 2-4 years of experience in building quantitative financial models and global macro market.
• Experience in programming in Python. Experience in database manipulation (SQL).
• Experience in handling large data set. Extract robust statistical signal from noisy data sets.
• Experience in applying Machine Learning or AI models in quantitative research.
• Ability to conduct in-depth research independently, provide creative solutions, and communicate ideas clearly.

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.