Ingénieur Data Scientist & AI

• Work closely with the marketing team to provide segment-specific recommendations and insights, aiming to improve targeting efficiencies and yield.
• Coordinate tightly with the Customer Value Management (CVM) team to develop cross-functional shared business use cases.
• Develop strategic analytical models, including customer 360, Lifetime Value (LTV), survival models, and customer segmentations.
• Create use cases for sales and distribution, leveraging spatial data, network (NW) data, and competitive customer data.
• Extract (or "Leverage") social media data for targeted communication, customer sentiment analysis, and the identification of behavioral and interest-based communities for digital targeting.
• Oversee Data Ecosystem Development & Maintenance (Technology).
• Collaborate closely with the Technology BI team on all matters related to data, platforms, and software.
• Identify and integrate new data sources into the Big Data platform.
• Manage governance and access rights for Advanced Analytics (AA) platforms and data.
• Ensure the integration and sharing of Advanced Analytics (AA) model outputs with various customer touchpoints and IT systems.

Experience & Background

• 0 to 3 years of hands-on experience in Data Mining, Big Data, and Advanced Analytics.
• Proven development experience with leading data science tools.
• Prior experience in the telecommunications sector is a significant asset.

Analytical & Technical Expertise

• Strong statistical and analytical skills.
• Solid theoretical and practical knowledge of various Machine Learning (ML) techniques and algorithms, including but not limited to: Regression, Random Forest, Text Mining, and Social Network Analysis.
• Ability to process and analyze large volumes of data using Big Data technologies.
• Proficiency in data visualization techniques.
• Good data engineering skills for both structured and unstructured data.

Business Acumen & Problem Solving

• Good understanding of Telco Commercial practices.
• Proven ability to translate complex business requirements into clear analytical model specifications.
• Proclivity for experimenting with data science methodologies and exploring new approaches.

Tools & Technologies

• Familiarity with, or proficiency in, programming languages commonly used in Data Science, such as Python and R.
• Exposure to statistical software like SAS and SPSS is a plus.
• Proficiency in data visualization tools such as Power BI and Tableau.
• Competency in Microsoft Office Suite.

Education & Certifications

• Master's Degree in Statistics, Computer Science, Operations Research (RO), or a related quantitative field.
• Certifications in leading data science tools are highly desirable.

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