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Job DescriptionYou will:• Partner with Ericsson’s Automation and Analytics team and digital transformation unit to prioritize and answer the most important questions where machine learning and AI breakthroughs will have material impact on Ericsson’s technology and business performance.• Use your experience in analytics tools and scientific rigor to produce actionable insights.This includes working with network, operational 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 processes, products, features, policies, and models• Mentor junior data scientists on technologies, methodologies and best-practices• Develop methodologies, standards/best-practices and systems for reusable ML and AI assets across Ericsson businesses• Develop and nurture ML and AI 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 ML and AI needs• Present and be prominent in ML and AI related forums and conferences, e.g., presenting papers, organizing sessions and be a panelistQualifications• BS, MS, or PhD in Computer Science, Computer Engineering, Mathematics, Physics, Economics, or related field; Certifying MI MOOCS can be compliment• 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 (Python, Scala, 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 jobEven better:• 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 experienceTechnologies we use and teach:• R, Python• Hive and other Bigdata family• Statistics (Frequentist/Bayesian methods, experimental design, causal inference)• Shiny/D3/Tableau etc Location: Kista, Stockholm, Sweden 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.