Senior Specialist Data Science
Ooredoo Group of Companies
من Indeed
هذه الوظيفة منشورة على Indeed،
ومجمّعة هنا لتسهيل البحث. التقديم يتم مباشرة على موقع الناشر.
تفاصيل الوظيفة
About Us:
Ooredoo is a dynamic global Telecommunications player operating in 10 countries serving more than 138 million customers. Ooredoo Qatar employs approximately 1,600 people driving Ooredoo to be the number one choice for world-class communications services in Qatar, and it is a team that you can be part of!
About the Business Unit:
Ooredoo places strong emphasis on a data-driven culture. In an ever-changing business landscape, there is increasing organizational focus on using AI/ML in day-to-day practice to create value, efficiency, and diversification.
The AI Hub division is responsible for putting in place and executing the data & AI roadmap, business plan, and strategy. Ooredoo is building a cloud platform-based solutions involving GCP that hosts the data platform and supports analytics and ML workloads, while Azure hosts GenAI and agentic AI workloads.
About the Role:
This role is responsible for strengthening the AI practice by working closely with the relevant stakeholders (B2B/C, Technology, Finance etc.) on impactful AI/ML and GenAI use cases to contribute to business strategy, digital growth and an evolving data & AI roadmap. The role will focus on translating customer and commercial needs into scalable ML/Gen AI/Data Science models and decisioning capabilities across business domains including customer value management (CVM), marketing, digital sales and customer care. For more details, please click here.
About You:
•
10 years' experience in a similar role.
•
Prior experience in data science and AI/ML-based advanced analytics, including hands-on development on leading data science platforms (Dataiku as the primary platform) using Python and R.
•
Demonstrated expertise in predictive modelling for telecommunications (telco) customer analytics (e.g., churn prediction, propensity, customer lifetime value (CLV), as well as segmentation, recommendation/decisioning systems, and time-series forecasting.
•
Experience implementing hyper-pers
Ooredoo is a dynamic global Telecommunications player operating in 10 countries serving more than 138 million customers. Ooredoo Qatar employs approximately 1,600 people driving Ooredoo to be the number one choice for world-class communications services in Qatar, and it is a team that you can be part of!
About the Business Unit:
Ooredoo places strong emphasis on a data-driven culture. In an ever-changing business landscape, there is increasing organizational focus on using AI/ML in day-to-day practice to create value, efficiency, and diversification.
The AI Hub division is responsible for putting in place and executing the data & AI roadmap, business plan, and strategy. Ooredoo is building a cloud platform-based solutions involving GCP that hosts the data platform and supports analytics and ML workloads, while Azure hosts GenAI and agentic AI workloads.
About the Role:
This role is responsible for strengthening the AI practice by working closely with the relevant stakeholders (B2B/C, Technology, Finance etc.) on impactful AI/ML and GenAI use cases to contribute to business strategy, digital growth and an evolving data & AI roadmap. The role will focus on translating customer and commercial needs into scalable ML/Gen AI/Data Science models and decisioning capabilities across business domains including customer value management (CVM), marketing, digital sales and customer care. For more details, please click here.
About You:
•
10 years' experience in a similar role.
•
Prior experience in data science and AI/ML-based advanced analytics, including hands-on development on leading data science platforms (Dataiku as the primary platform) using Python and R.
•
Demonstrated expertise in predictive modelling for telecommunications (telco) customer analytics (e.g., churn prediction, propensity, customer lifetime value (CLV), as well as segmentation, recommendation/decisioning systems, and time-series forecasting.
•
Experience implementing hyper-pers