Science team at AIG develops AI-first products (apps and services that use machine learning to inform and assist their users) for both insurance and investment arms of AIG.
As a critical role in Science’s success, we are looking to hire NLP scientists – with Deep Learning expertise – to join our team
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.
Day-to-day Activities Include, Amongst Others
Core scientific research and development in NLP, machine learning, deep learning, text analytics, and data mining
Machine learning engineering
Collaboration with the rest of the team (e.g. machine learning scientists, full-stack developers, test engineers, product managers, project managers, and higher business functions) within specific projects and for specific products
Both senior candidates (e.g., with years of post-doctoral and/or commercial experience) and junior candidates are welcome to apply; we have and will offer positions appropriate to expertise and level of experience.
AIG Europe (Services) Limited
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).
An ideal candidate (is not required to, but) will also have
Track record in integrating deep learning with real-time computing (including mobile apps and front-end systems).
Experience in employing deep learning in a commercial setting – in collaboration with product development teams.
Experience in one or more the following: knowledge extraction and knowledge graphs; text summarisation; semantic role labelling; information retrieval; Natural Language
Generation; NLP resource development and management, crowdsourcing; NLP evaluation and testing.
Publication record in (and willingness to represent AIG in) top machine learning and NLP journals (e.g., CL, NLE, AI, TPAMI, IJCV, and JMLR) and conferences (e.g., N.I.PS, ICML,
ACL, EMNLP, NAACL, EACL, COLING, LREC, CoNLL, IJCNLP, ICLR, and IJCV).
Experience of working with engineering and design / product teams.
Broad knowledge of machine learning (including topics such as graph theory, hierarchical modeling, and Bayesian inference).
Practical experience of modern big-data computing ecosystems (e.g., Apache Spark).
The ability to engage with business stakeholders (with excellent oral and written communication skills).