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Sr. Data Scientist (VOIS) at Vodafone United States
Giza, Giza, Egypt -
Full Time

Start Date

Immediate

Expiry Date

04 Jan, 26

Salary

0.0

Posted On

06 Oct, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Data Science, Analytics Models, SQL, NoSQL, Statistical Models, Predictive Models, Machine Learning, Python, R, SAS EM, MATLAB, SPSS, Hadoop, Spark, Teradata, Oracle

Industry

Telecommunications

Description
Build, test, and deliver new analytics models using diverse datasets to support business use cases. Prepare and cleanse data, optimise SQL and NoSQL queries, and implement statistical models. Develop descriptive and predictive models for specific market segments, products, and services. Collaborate with commercial teams to present actionable insights derived from analytical results. Support product management in identifying customer-facing analytics use cases for roadmap planning. Contribute to defining lifecycle processes for analytics to enable fast and cost-effective development. Participate in business analysis and document specifications for information requests. Experienced in advanced analytics techniques including descriptive, predictive, and optimisation models. Skilled in handling complex, high-volume data, preferably within Hadoop/Spark environments. Proficient in SQL and databases such as Teradata, Oracle, and MySQL. Strong knowledge of machine learning techniques and their real-world applications. Hands-on experience with statistical tools like Python, R, SAS EM, MATLAB, SPSS, and Teradata Warehouse Miner. Holds a degree in Computer Science, Engineering, Applied Mathematics, or Statistics (MS/Ph.D. preferred). Comfortable working in international, distributed teams with agile methodologies. Excellent communication, analytical, and troubleshooting skills. Innovative thinker with a flexible, proactive attitude and strong coordination capabilities. Fluent in English, both written and spoken.

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Responsibilities
Build, test, and deliver new analytics models using diverse datasets to support business use cases. Collaborate with commercial teams to present actionable insights derived from analytical results.