Deep Learning Lead (VP) - job id 32265

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Deep Learning Lead (VP)

F/T Employee

Midtown NYC on 7th

How to Apply


Claire Volis


(732) 757-2841


(212) 616-4800 ext-590

A F/T position at a global financial services firm.

Pay Options: F/T Employee.

Contact Claire Volis. call (732)791-4721 / (212)616-4800 ext.590 or email with the Job Code CV32265 or Click the Apply Now button ().

Location: Midtown NYC on 7th.

Skills required for the position: MACHINE LEARNING, artificial intelligence, DEEP LEARNING.

Detailed Info:

Machine Learning is the 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

Development/Computing Environment: Required

- 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 novel 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

- Research publications.

The position offers competitive compensation package.

Job Id: 32265