Deep Learning Lead (VP)
New York, NY
Brokerage is a leading global financial services firm providing a wide range of investment banking, securities, investment management and wealth management services. The Firm' s employees serve clients worldwide including corporations, governments and individuals from more than 1, 200 offices in 43 countries.
As a market leader, the talent and passion of our people is critical to our success. Together, we share a common set of values rooted in integrity, excellence and strong team ethic. Brokerage can provide a superior foundation for building a professional career - a place for people to learn, to achieve and grow. A philosophy that balances personal lifestyles, perspectives and needs is an important part of our culture.
Technology works as a strategic partner with Brokerage business units and the world' s leading technology companies to redefine how we do business in ever more global, complex, and dynamic financial markets. Brokerage' s sizeable investment in technology results in quantitative trading systems, cutting-edge modeling and simulation software, comprehensive risk and security systems, and robust client-relationship capabilities, plus the worldwide infrastructure that forms the backbone of these systems and tools. Our insights, our applications and infrastructure give a competitive edge to clients' businesses— and to our own.
Brokerage Machine Learning (MSML) is Brokerage' s center of excellence responsible for working with business and IT teams across the firm to solve mission-critical problems. We are a highly motivated and collaborative team consisting of data scientists, machine learning engineers and members from academia. Our team is uniquely positioned to apply advanced AI to revenue generating business cases.
- Lead machine learning projects and develop models in collaboration with strats, quants and traders
- Independently work on end-to-end development of models using trading data, market data, alternative data and data from other internal/external data sources
- Bring deep learning insight into econometric modeling
- Mentor and lead relatively junior members of the team
- Work with stakeholders to refine requirements and communicate progress
- Rapidly prototype and iteratively develop models
- Deploy models to production and monitor performance
- Study recent research and develop original ideas to solve hard problems
- Speak in internal and external forums
- Research level understanding of deep learning architectures, their applicability to data and optimal training strategies
- In-depth knowledge of deep learning networks like DNN, CNN, RNN, Auto Encoder, GAN and VAE
- At least 3 years of exclusive experience in deep learning
- Strong command over linear algebra and statistics
- Ability to quickly translate ideas to efficient, elegant code in Python and Tensorflow
- Research oriented mindset with a natural ability to combine traditional techniques and cutting-edge research to develop Client models
- Experience in model training in a GPU environment
- Experience in leading a team of researchers and research engineers
- 5-10 years of machine learning experience
- Excellent communication and presentation skills
- MS in Computer Science, Statistics, Financial Engineering or a related quantitative field. PhD preferred.
- Experience in time series analysis and sequential data using ARIMA, Kalman Filters, HMM, RNN etc.
- Reinforcement learning experience in continuous state/action space is highly desirable
- Experience in Bayesian Modeling using MCMC, SeqMC and newer techniques like Variational Bayes