A global leader in consulting, technology services and digital transformation, Capgemini is at the forefront of innovation to address the entire breadth of clients’ opportunities in the evolving world of cloud, digital and platforms. Building on its strong 50-year heritage and deep industry-specific expertise, Capgemini enables organizations to realize their business ambitions through an array of services from strategy to operations. Capgemini is driven by the conviction that the business value of technology comes from and through people. It is a multicultural company of 200,000 team members in over 40 countries. The Group reported 2017 global revenues of EUR 12.8 billion.
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Job Title: Data Scientist
Position Type: Permanent/Fulltime
An advanced degree in a numeric discipline (e.g., Statistics, Machine Learning, Computer Science, Engineering, and Physics).
Scientific expertise, strong track record, and real-world experience in Machine Learning and Deep Learning, especially with hands-on experience in hyper-parameter tuning and deep construction / distribution (e.g., architecture design in DNN/CNN/RNN, parameter initialization, activation, normalization, and optimization).
Expertise in programming (e.g., Python and C++) and computing technologies (high-performance computing, e.g., CUDA).
Ability to use existing deep / machine learning libraries (e.g., Python, TensorFlow, Torch, Theano, Caffe, and scikit-learn).
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).
Employ the best of Machine Learning research for solving business problems in Banking and Capital Market.
Build and refine machine Learning/deep learning algorithms that can find “useful” patterns in large multi-modal data (particularly, images, text, conversations, and transactional data).
Communicate (both oral and written) with clients
For more senior candidates: Lead, inspire and mentor junior scientists and research assistants / interns.
Natural Language Processing (NLP).
Deep Reinforcement Learning, Unsupervised Learning, and Generative models.
Recurrent Neural Networks, Sequence Learning and Sequence Analysis.
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