At Ericsson, you can be a game changer! Because working here isn’t just a deal. It’s a big deal. This means that you get to leverage our 140+ years of experience and the expertise of more than 95,000 diverse colleagues worldwide. As part of our team, you will help solve some of society´s most complicated challenges, enabling you to be ‘the person that did that.’ We’ve never had a greater opportunity to drive change; setting the bar for technology to be inclusive and accessible; empowering an intelligent, sustainable, and connected world.

Are you in?

Master Thesis: Machine learning models management in industrial scenario

Job Description

Date: Nov 6, 2019

Do you want to do your thesis with us in Göteborg? 

Would you like the chance to do your thesis together with one of the world’s leading technology companies? We are on a quest to enable communication for everyone and everything and we believe that we do that by being as innovative as possible. If you want to help us to be even more innovative – then you are on the right track! 

 

We want you to become a part of a truly global company working across borders in 180 countries, offering a diverse, performance-driven culture & an innovative & engaging environment where employees enhance their potential every day. Come and learn and grow with us at Ericsson. 

 

Background

In software engineering, CI/CD generally refers to the common practices of continuous integration and continuous delivery, so all changes are always reviewed and tested.

In our team, we are thriving to improve HW life cycle management by data sciences and have implemented several machine learning models for different business cases, like hardware troubleshoot automation, production deviation detection, etc. 

Current management of machine learning models in production requires significant amount of manual work as data is constantly changing and evaluation of different machine learning models is not done systematically. We would like to bring CI/CD concept to machine learning models management based on AB test/multiarmed bandit as part of the evaluation as it has emerged as the predominant method of online testing in the industry today.

 

Scope

Machine learning models performance is compared fairly and a decision is made about whether the new model performs substantially better than the old model. Different AB tests and multiarmed bandit solutions in statistics will be investigated.

In addition, trouble report system is used today internally to troubleshoot hardware or software issues occurring in a hardware at customer unit. Data from this source will be investigated, and new potential features / data points will be evaluated whether and how it can improve current model by the previous developed methodology. Different machine learning models will be tested and modified to needs if necessary.

Improving current models performance is key and evaluation needs to be done in a fair manner. 

 

Desired qualifications

We are looking for two driven individuals with background in physics/statistics/computer science or a related numerical field at a master’s level together with strong coding skills and problem solving skills. A strong interest and skillset in the field of machine learning and/or statistical modelling is highly advantageous. 

 

Application

This project is suited for two students amount to 60 ECTS credits. The project is planned to start during the first half of January 2020 and the estimated end date is end of June 2020. 

If you feel interested, please send in your application as soon as possible.

While applying please attach your updated CV, current grades and cover letter written in English into one document (under CV field in the application tool) and clearly define your technical knowledge.

 

If you have any additional questions, please contact our Recruiter Elzbieta Penpeska at elzbieta.penpeska@ericsson.com

 

Literature

Steven L. Scott. 2015. Applied Stochastic Models in Business and Industry, vol. 31 (2015), pp. 37-49

https://www.gartner.com/binaries/content/assets/events/keywords/catalyst/catus8/preparing_and_architecting_for_machine_learning.pdf

 

Ericsson provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, sexual orientation, marital status, pregnancy, parental status, national origin, ethnic background, age, disability, political opinion, social status, veteran status, union membership or genetics.

Ericsson complies with applicable country, state and all local laws governing nondiscrimination in employment in every location across the world in which the company has facilities. In addition, Ericsson supports the UN Guiding Principles for Business and Human Rights and the United Nations Global Compact.

This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, training and development.

Ericsson expressly prohibits any form of workplace harassment based on race, color, religion, sex, sexual orientation, marital status, pregnancy, parental status, national origin, ethnic background, age, disability, political opinion, social status, veteran status, union membership or genetic information.

 

Primary country and city: Sweden (SE) || || Göteborg || Stud&YP

Req ID: 304363