Date: May 16, 2019
Ericsson is world's leading provider of communications technology and services. Our offerings include services, consulting, software and infrastructure within Information and Communications Technology.
Using innovation to empower people, business and society, Ericsson is working towards the Networked Society: a world connected in real time that will open up opportunities to create freedom, transform society and drive solutions to some of our planet's greatest challenges.
We are truly a global company, operating across borders in over 180 countries, offering a diverse, performance-driven culture and an innovative and engaging environment. As an Ericsson employee, you will have freedom to think big and the support to turn ideas into achievements. Continuous learning and growth opportunities allow you to acquire the knowledge and skills necessary to progress and reach your career goals. We invite you to join our team.
* Partner with Ericsson's machine intelligence (MI) team to prioritize and answer the most important questions where machine learning and AI breakthroughs will have material impact
* Use your experience in analytics tools and scientific rigor to produce actionable insights. This includes working with petabytes of 4G/5G-netwroks, IoT and exogenous data, and proposing/selecting/testing predictive models, recommendation engines, anomaly detection systems, statistical model, deep learning, reinforcement learnings and other machine learning systems
* Collaborate with business leaders, subject matter experts, and decision makers to develop success criteria and optimize new products, features, policies, and models
* Mentor junior data scientists on technologies, methodologies and best-practices
* Develop methodologies, standards/best-practices and systems for reusable MI assets across Ericsson businesses
* Develop and nurture MI communities within Ericsson and its ecosystem in the areas of expertise
* Collaborate with others on best way to document and present the findings and experiments of the data analysis, including benchmarking against (de-facto) industry standards and baselines, to the stakeholders
* Collaborate with product development teams to industrialize the findings
* Communicate key results to senior management in verbal, visual, and written media
* Engage with external ecosystem (academia, technology leaders, open source etc.) to develop the skills and technology portfolio for MI's needs
* Present and be prominent in Machine learning related forums and conferences, e.g., presenting papers, organizing sessions and be a Panelist
Technologies we use and teach:
* R, Python
* Hive and other Bigdata family
* Statistics (Frequentist/Bayesian methods, experimental design, causal inference)
* Shiny/D3/Tableau, etc
* BS, MS, or PhD in Computer Science, Mathematics, Physics, Economics, or related field
* 5 to 7 years of Applied Data Science experience
* Strong knowledge in Statistics, e.g., hypothesis formulation, hypothesis testing, descriptive analysis and data exploration.
* Demonstrated skills in Machine Learning, e.g., linear/logistics regression discriminant analysis, bagging, random forest, Bayesian model, SVM, neural networks, etc.
* Strong Programming skills in various languages (C++, Scala, Java, R)
* Strong skills in the use of current state of the art machine learning frameworks such as Scikit-Learn, H2O, Keras, TensorFlow, and Spark
* Ability to clearly communicate complex results to technical and non-technical audiences
* Versatility and willingness to learn new technologies on the job
* Certification: Machine Learning MOOCS
* Familiarity with Linux/OS X command line, version control software (git), and general software development
* Experience in programming or scripting to enable ETL development
* Familiarity with relational databases
* Previous industry experience or internships in product related analytics
* Independent research experience
DISCLAIMER: The above statements are intended to describe the general nature and level of work being performed by employees assigned to this classification. They are not intended to be construed as an exhaustive list of all responsibilities, duties and skills required of employees assigned to this position. Therefore employees assigned may be required to perform additional job tasks required by the manager.
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