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:
- Incubation of disruptive innovation (via scientists, engineers and designers working together)
- Machine learning R&D (and publication in top AI/ML conferences and journals)
- 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 NLP (and 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.
- Experience in core NLP and text analytics tasks and application areas (e.g., text classification, topic detection, information extraction, Named Entity recognition, entity resolution, Question-Answering, dialog systems, chatbots, sentiment analysis, event detection, language modelling).
- 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).
- Familiarity with existing Open Source NLP libraries and utilities (e.g., Stanford CoreNLP, spaCy, fastText, AllenNLP, PyTorch-NLP, Gensim, word2vec, GloVe). Experience in mining large-scale, multi-domain text corpora and streams.
- 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
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