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Data Scientist at Vodafone United States

Milan, Lombardy, Italy -

Full Time

Start Date

Immediate

Expiry Date

22 Jan, 26

Salary

0.0

Posted On

24 Oct, 25

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Science, Machine Learning, Big Data, Analytics, SQL, Python, Pyspark, NLP, Neural Networks, Random Forest, SVM, Anomaly Detection, Gradient Boost, DevOps, Data Management, Cloud Technologies

Industry

Telecommunications

Description

Key accountabilities and decision ownership : Identification of data science / big data / analytics use cases for Network Operations and architectural High Level Design Choice and implementation of the best machine learning algorithm suited to the use case Industrialization of the use cases on Cloudera, Openshift/Kubernetes or on AWS/Google cloud environments, with the support of data engineers Technical leadership in analysis and data management domains Data-driven evaluation of vendor product adoption Experience in Machine Learning SW development and data analysis Experience in designing and implementing use cases over big data architectures involving massive data volume, also under real-time constraints Knowledge of pros and cons of existing data storage technologies (relational DB, Big Data Frameworks, no-SQL DB on cloud and on prem) Degree in Computer Science, Maths, Engineering or equivalent Junior Profile with Experience in similar position (Max 2 years) SQL, Python (Pandas, Tensorflow, Scikit-learn, and main other ML libs), Pyspark and SW development capabilities Machine learning algorithms knowledge (NLP, Neural Networks., Random Forest, SVM, Anomaly Detection especially on time series, Gradient Boost and all other main ML models both supervised and unsupervised) Knowledge of deployment best practices and DevOps pipeline Excellent analytics and mathematics skills Professional English (spoken and written)

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Responsibilities

The role involves identifying data science use cases for Network Operations and implementing machine learning algorithms. It also includes industrializing these use cases on various cloud environments with the support of data engineers.