Qualifications & Experience Typically possesses degree in Computer Science, Mathematics, Machine Learning, Operation Research, and Statistics or equivalent expertise. Key skills you will need (bring some, develop others) • Use of analytics / machine learning techniques to derive insights from large complex and multi-dimensional dataset including data cleansing. • Developing new models using both existing technologies as well as derive new R&D efforts across different disciplines. • Strong skills in bringing theoretical ideas to proof of concept • Creating examples, prototypes, extensions for proof of concepts to demonstrate effectiveness of the technologies and models. • Ability to clearly communicate concepts (including visuals) and key insights within the research team and the communities. • Demonstrated skill in the use of analytics tools or languages (e.g. R, Python, Matlab) • Knowledge of (non-)relational and graph databases as well as distributed storage and processing environments (e.g. InfluxDB, OpenTSDB, RethinkDB, Neo4j, OrientDB). • Software engineering skills (e.g. test driven development, Agile methodologies, build and integration automation) Desired additional skills • Understanding of artificial intelligence, deep learning and modelling neural networks • Background in delivering UX for dashboards • Usage and advancement of monitoring and instrumentation frameworks (e.g. collectd, snap, DTrace, eBPF) • Knowledge of resource managers (e.g. OpenStack, Mesos, Kubernetes, VMware) The candidate should have a desire to work in a test-driven, collaborative and iterative engineering environment. The candidate should love learning, data, scale and agility. The candidate should excel at making complex concepts simple and easy to understand by those around him/her in the team.