Machine Learning Scientist - Reinforcement Learning
Sumner & Scott are looking for a Machine Learning Scientists to join our clients team and contribute to the development and implementation of the algorithmic core of a series of exciting new projects in their global Science team – focused on solving complex business problems.
We are looking for specific experience in Reinforcement Learning.
Success in this role requires the candidate to:
1) Employ the existing (and develop new) Machine Learning algorithms that can find patterns in large multi-modal data.
2) Innovate and provide solutions for the business (e.g., by translating complex commercial problems to Machine Learning problems).
3) Be an active member of teams that provide the business with data-driven apps, insight and strategies.
4) Participate in, lead, and create cross-functional projects and training.
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 and start-up-like division of the world’s largest insurer; and create game-changing products for insurance (InsTech) and financial industry (FinTech).
Both senior candidates (i.e., with years of post-doctoral and/or industrial experience) and junior candidates (i.e., recent PhD graduates) are welcome to apply; we have and will offer positions appropriate to expertise and level of experience.
The minimum required skills include:
1) Scientific expertise and applied experience in Machine Learning (ideally, a combination of excellent academic research and high-impact commercial experience).
2) In depth understanding of common Machine Learning algorithms (e.g., for classification, regression and clustering).
3) Track record in advanced topics of Machine Learning (e.g., Bayesian inference, hierarchical models, deep learning, Gaussian processes, causal inference, graph theory, etc.).
4) Advanced programming skills in Python and/or R (and their related data processing, Machine Learning, and visualisation libraries).
5) Practical experience in preparing data for Machine Learning (e.g., using SQL and/or NoSQL technologies).
6) Completion of at least one significant project (equivalent of a great PhD research project, and/or a viable commercial product) in applied Machine Learning.
7) Excellent communication skills
The ideal candidate would also have experience in:
1) Integration of Machine Learning algorithms with big-data platforms (e.g., Spark) and high-performance computing ecosystems (e.g., CUDA).
2) Programming in C++, Java.
3) Deployment of algorithms as realtime/ highly available services.
4) Integration with front-end systems (e.g., HTML5/ native mobile apps).
5) Employing Machine Learning in collaborative commercial settings (e.g., using DevOps methodologies and tools such as GitHub), ideally, in collaboration with product development teams.
6) Publication in the top scientific journals and conferences.
7) Leading scientific projects.
8) Publication record
We create an experience based on honesty, efficiency and integrity and having expertise within this niche means that we will give you realistic solutions to your recruitment needs.