Location: New York, New York, United States
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Two Sigma is different from other investment firms. Founded by a statistician and a computer scientist, we specialize in the advanced application of technology, data, and evidence to optimize outcomes in many domains. The global markets fuel our imagination with unlimited information to study, countless efficient ways to take action, and meaningful opportunity to improve by iteration. We draw ideas and inspiration from the broader computer science, mathematics, statistics, and investment communities. We strive to use data and large scale machine learning techniques to model and understand the financial world around us.
When you work with us, you get to tackle diverse, tough problems alongside other scientists and engineers. People who will challenge your ideas and with whom you can really collaborate. And you’re doing work that matters to a lot of people, too. Our clients 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 to help them achieve their goals.
We are looking for creative experts who are interested in applying general machine learning and specifically deep learning techniques to many types of problems, but particularly those with large amounts of noisy data. Your job will be to develop effective techniques and infrastructure, from the initial idea to the running prototype and product. Similar to a research environment, you will write code, use the latest machine learning tools, run experiments, and generally develop techniques and processes to improve our understanding of how financial data influences the world around us. You will partner with teams across Two Sigma to implement your ideas into their products. As part of this team you'll also remain connected to the broader research community by partnering with internal and external collaborators and participating in relevant conferences.
Excellent programming skills in Python, C++, Tensorflow, PyTorch or similar languages
Background in machine learning techniques with large amounts of noisy data, curiosity in applying it to financial problems
Relevant research experience
Advanced degree in Computer Science, Engineering, or other STEM field
Internships/work experience with machine learning, deep learning, reinforcement learning
Publications at NeurIPS, ICML, ICLR or similar
Experience with cloud environments and multi-machine setups
Participation in open source community