Deep Learning Engineer – Finance

Deep Learning Engineer – Finance

Our clients science team develops AI-first products (apps and services that use machine learning to inform and assist their users) for both their insurance and investment arms. This is mainly achieved through:

1. Incubation of disruptive innovation (via scientists, engineers and designers working together)
2. Machine learning R&D (and publication in top AI/ML conferences and journals)
3. Provision of machine-learning advisory / consulting for global businesses.

This is an exciting opportunity for those who want to enjoy state-of-the-art R&D and be challenged and grow as a Deep Learning Scientist; along the way this role will contribute to game-changing products for the multi-trillion-dollar insurance industry and use our clients global network to deliver impact and change.

Responsibilities and Performance Objectives 

• Employ the best of Deep Learning research for solving business problems – disrupting the current practice in insurance and investment.
• Build and refine algorithms that can find “useful” patterns in large multi-modal data (particularly, text, conversations, and transactional data).
• Provide the business with new product ideas, as well as data-driven apps, insights and strategies.
• Communicate (both oral and written) with colleagues and stakeholders (both internal and external).
• For more senior candidates: Lead, inspire and mentor junior scientists and research assistants / interns

The required skills include:

• An advanced degree in a numeric discipline (e.g., Statistics, Machine Learning, Computer Science, Engineering, and Physics).
• Completion of one significant project (equivalent of a PhD research project, and/or a viable commercial product) in one or more of the hiring themes.
• Scientific expertise, strong track record, and real-world experience in Deep Learning, especially with hands-on experience in hyper-parameter tuning and deep construction / distribution (e.g., architecture design in CNN/RNN/LSTM, attention mechanisms, parameter initialization, activation, normalization, and optimization).
• Expertise in programming (e.g., Python, C++ or Java/Scala) and computing technologies (high-performance computing, e.g., CUDA).
• Ability to use existing deep / machine learning libraries (e.g., TensorFlow, Torch, Theano, Caffe, scikit-learn, Deeplearning4j, and Chainer).
• Experience with the data and platform aspects of the projects.
• Review, direct, guide, inspire the research of the more junior scientists in the team (especially applicable to more senior candidates)

#AI & Machine Learning 

#Computer Vision

Salary DOE. 

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