Student internship: Un-/Semi-supervised machine learning for real-time streaming analytics

     
Employer
LocationZurich, Zurich Region, Switzerland
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Activity
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Your Mission

Student internship: Un-/Semi-supervised machine learning for real-time streaming analytics

Ref. 2018-21

About the internship

Analysis of continuous and discrete valued multivariate time series is essential for intelligent management of complex systems in a range of industries, from IT equipment to cash machines and wind turbines. In the context of storage systems, detecting anomalous behavior in nonstationary multivariate time series sensor data in real time can provide service teams the ability to identify impeding performance issues in a proactive manner and act accordingly before the customer experiences them. We plan to apply state-of-the-art techniques to analyze streaming time series for hundreds of sensors to identify and locate anomalies across correlated metrics, even when they are deeply hidden in high-dimensional subspaces. We are looking for an intern with a relevant background to join us in developing a big data anomaly-detection pipeline.

The successful candidate will have the opportunity to participate in an advanced machine-learning project that aims for high business impact by detecting and (potentially) predicting performance anomalies in managed storage systems. Additionally, the candidate will gain valuable experience in processing large quantities of data to create actionable insights.

Requirements

Candidates are expected to have the following background and interests:

  • Hands-on data science on the prevalent platforms, as well as the basic fundamentals of machine learning
  • Proficiency in Python
  • Experience with anomaly-detection techniques would be a plus
  • Familiarity with big-data technologies such as Spark would be a plus.

Diversity

IBM is committed to diversity at the workplace. With us you will find an open, multicultural environment. Excellent flexible working arrangements enable both women and men to strike the desired balance between their professional development and their personal lives.

How to apply

If the above position fits your background and interests, please send your complete CV as well as contact information for three references to:

Claudia Longchamp
Human Resources

Contactwww.zurich.ibm.com , https://www.zurich.ibm.com/careers/
In your application, please refer to science-jobs.ch
and reference  JobID 41403.