Ingénieur Data Scientist & AI

الوصف

Missions :

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.

Profil :

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.

Autres :

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Ingénieur IA