About the job:
This role gives you the distinctive alternative to work on the interface of analysis and product to develop novel machine studying options for cloud infrastructure (AIOps).
We are a small staff in Office 365. We carefully collaborate with researchers and core engineering groups throughout the globe to develop resolution with excessive enterprise influence that permit to scale our service whereas lowering the price and sustaining reliability.
Office 365 is the biggest collaboration service on the earth with 100s of thousands and thousands of shopper/enterprise mailboxes, paperwork, and conversations, it represents the world’s largest platform of human collaboration for private, enterprise, and academic use. We are a massively distributed cloud service with exabytes of knowledge dealt with by 100s of 1000’s of servers in 100s of information facilities across the whole globe.
The ultimate candidate could have a robust background in machine studying/techniques analysis and the ambition to use this in manufacturing techniques. Some of the challenges are: how can we optimize the position and scheduling of dynamic Office workloads on distributed cloud infrastructure to enhance useful resource utilization. The algorithms we develop should seize the advanced behavior of service elements, dynamically adapt to altering situations, and should be examined/applied to manufacturing techniques without compromising consumer expertise.
- Conduct analysis to advance the cutting-edge in machine studying for techniques (AIOps)
- Apply that analysis to develop and deploy scalable fashions into manufacturing to influence billions of individuals utilizing Office 365.
- Collaborate with staff members from different analysis and engineering groups
- End-to-end execution of the information science course of, from understanding enterprise necessities, knowledge discovery, and extraction, mannequin improvement, and analysis
- Data evaluation of telemetry indicators and derive actionable insights
- There would be the alternative to publish and contribute to scientific conferences
- PhD in machine learning or statistics or associated self-discipline
- Broad and strong understanding of frequent statistical and machine studying strategies, each classical machine studying and deep studying
- Strong information in algorithms similar to time series evaluation, anomaly detection, Bayesian optimization, contextual bandits/reinforcement studying, semi-supervised machine studying, constraint optimization, and knowledge visualization
- Working information on cloud service infrastructure and system design is fascinating
- Experience with engaged on massive knowledge pipelines and cloud options
- Strong analytical, utilized analysis, and communication ability
- Ability to work independently and in staff, take initiative, and lead engagements as required
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