We are looking for a machine-learning scientist to lead the development and implementation of the algorithmic core of a series of exciting new projects in AIG's Special Projects Lab (part of AIG's Global Science team). The success in this role requires the ownership of a significant new workstream in the team focused on solving natural language process (NLP) and/or speech and signal processing (SSP) problems (including, but not limited to, data representation and predictive modelling - particularly, using deep learning) and high-performance computing (including, near real-time data analysis).
Success in this role require the candidate to: 1) Employ the best of NLP and SSP research for solving business problems - disrupting the current practice in insurance 2) Build and refine NLP/SSP and machine learning algorithms that can find patterns in large multi-modal data 3) Provide the business with data-driven apps, insights and strategies 4) Participate in, lead, create cross-functional projects 5) Communicate (both oral and written) with colleagues and stakeholders (both internal and external) 6) Review, direct, guide, inspire the research of more junior scientists in the team
Above all, this role will provide a unique opportunity to enjoy state-of-the-art research and development; grow and be challenged in an entrepreneurial / startup-like division of a large company; and create game-changing products for the insurance/financial industry.
The candidate must have: 1) Scientific expertise and real-world experience in deep learning (convolutional neural networks, restricted Boltzmann machines, and deep neural networks) - applied to NLP and/or SSP. 2) Strong background in basic machine learning and statistical modelling (e.g., classification, regression, and clustering) 3) Strong track record in related scientific fields (e.g., machine learning, computer science, engineering, statistics, and robotics) 4) Expertise in programming (e.g., C++, CUDA, Python, R and Java) and computing technologies (high-performance computing and/or big-data platforms) 5) Ability to use existing machine/deep learning libraries (e.g., Torch, Theano, Caffe, and SciKitLearn)
The Ideal candidate would also have: 1) Track record in integrating machine learning with real-time computing (including mobile apps and front-end systems) 2) Experience in employing machine learning in a commercial/business setting – in collaboration with product (back-end and front-end) development teams. 3) Experience in applying machine learning (ideally, deep learning) to problems in more than one domain (e.g., Vision, NLP, SSP, …). 4) Impressive publication record in the top scientific journals. 5) Publication record in (and willingness to represent AIG in) scientific conferences such as NIPS, ICML, ACL, EMNLP, NAACL, EACL, COLING, SIGIR, ICCV, ECCV, ICLR, and CVPR. 6) Broad knowledge of machine learning (including topics such as graph theory, hierarchical modelling, and Bayesian inference) 7) Practical experience of modern big-data computing ecosystems such as Apache Spark 8) Proven track record in leading scientific projects
In order to apply, please follow the link here. For any (informal) inquiries, please feel free to contact Hannah Gibson (hannah.gibson@aig.com).
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Reza Khorshidi, D.Phil. (Oxon)
Head, Special Projects Lab | Director (EMEA), Quantitative Analytics