About this opportunity

What you will do:

Analyze data using statistical techniques and build machine learning models to derive actionable management insights and solutions. Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability. Build the infrastructure required for optimal extraction, transformation, and loading of data from data sources using SQL and cloud-based ‘big data’ technologies from AWS and Azure. Build machine learning models for classification and regression to discover predictive insights related to network and business objectives. Explore and work on Big Data tools and technologies to sustain continued operational transformation objectives and market leadership. Work with stakeholders including the Executive, Product, Data and Design teams to assist with data-related technical issues and support data infrastructure needs. Keep data separated and secure across national boundaries through multiple data centers and strategic customers/partners. Create tool-chains for analytics and data scientist team members that assist in building and optimizing the project. Work with data and machine learning experts to strive for greater functionality in our data and model life cycle management systems. Support dataOps competence build-up in Ericsson Businesses and Customer Serving Units. Research and develop approaches on how to improve business processes and customers’ experience by using data. Select and test predictive models, recommendations, anomaly detection, deep learning or reinforcement learning.

You will bring:

Master’s degree (or foreign equivalent) in Business Analytics or a related field and completion of 1 internship involving the following: performing data mining and feature engineering; building models and tuning parameters using linear or logistic regression and neural networks; using Python and/or R or Jupyter notebooks; developing solutions using automation scripts and Machine Learning techniques to leverage hyperparameter optimization techniques; and, testing models in Scikit-Learn, TensorFlow or Spark environments. In lieu of the completion of 1 internship, employer will also accept 1 year of industry experience involving the following: performing data mining and feature engineering; building models and tuning parameters using linear or logistic regression and neural networks; using Python and/or R or Jupyter notebooks; developing solutions using automation scripts and Machine Learning techniques to leverage hyperparameter optimization techniques; and, testing models in Scikit-Learn, TensorFlow or Spark environments.

Why Join Ericsson? At Ericsson, you´ll have an outstanding opportunity. The chance to use your skills and imagination to push the boundaries of what´s possible. To build never seen before solutions to some of the world’s toughest problems. You´ll be challenged, but you won’t be alone. You´ll be joining a team of diverse innovators, all driven to go beyond the status quo to craft what comes next.

#LI-DNI

Encouraging a diverse and inclusive organization is core to our values at Ericsson, that's why we nurture it in everything we do. We truly believe that by collaborating with people with different experiences we drive innovation, which is essential for our future growth. We encourage people from all backgrounds to apply and realize their full potential as part of our Ericsson team. Ericsson is proud to be an Equal Opportunity and Affirmative Action employer, learn more. If you need assistance or to request an accommodation due to a disability, please contact Ericsson at hr.direct.mana@ericsson.com

DISCLAIMER: The above statements are intended to describe the general nature and level of work being performed by employees in this position. They are not an exhaustive list of all responsibilities, duties and skills required for this position, and you may be required to perform additional job tasks as assigned.

Primary country and city: United States (US) || United States : Texas : Plano