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 creative experts who are interested in applying machine learning to many types of problems, but particularly those with large amounts of noisy data.
When you work with us, you get to tackle tough problems alongside other scientists and engineers. People who will challenge your ideas. Who you can really learn from, and collaborate with. And you’re doing work that matters to a lot of people, too. Our investors include some of the world’s largest retirement funds, research institutions, educational endowments, healthcare systems and foundations. We admire what they do, and we’re proud to work with these organizations.
You will take on the following responsibilities:
- Use a rigorous scientific method to develop sophisticated trading models and shape our insights into how the markets will behave
- Apply machine learning to a vast array of datasets
- Create and test complex investment ideas, use the latest machine learning tools, write code and partner with our engineers to test your hypotheses
- Join our reading circles to stay up to date on the latest research papers in your field
- Attend academic seminars to learn from thought leaders from top universities
- Share insights from conferences focused on statistics, machine learning, and data science
You should possess the following qualifications:
- Degree in a technical or quantitative discipline, like statistics, mathematics, physics, electrical engineering, or computer science (all levels welcome, from bachelor’s to doctorate)
- Strong skills in at least one programming language (like C, C++, Java, or Python)
- Understanding of the ins and outs of machine learning algorithms—and can tweak them as needed
- Experience with applied machine learning to real-world datasets at scale
- Published your work in journals and/or have presented at conferences
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.