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 RoleThis role is responsible for strengthening the AI practice by working closely with the relevant stakeholders (B2 B/C, Technology, Finance etc.) on impactful AI/ML and Gen AI 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.
Functional ContextOoredoo 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 cloud platform‑based solutions involving GCP that hosts the data platform and supports analytics and ML workloads, while Azure hosts Gen AI and agentic AI workloads.
Role Accountabilities Understand business requirements for Telecom business (B2 C/B2 B) and develop AI/ML based data science models to do value addition. Own end‑to‑end AI/ML model lifecycle: problem framing, data discovery, feature engineering, training, validation, deployment, monitoring, and continuous improvement (self‑learning where applicable). Deliver Customer Value Management (CVM) use cases such as churn prediction and prevention, customer lifetime value (CLV), propensity models for upsell/cross‑sell, and Next Best Offer / Next Best Action frameworks. Enable hyper‑personalization and real‑time decisioning by designing decision logic, recommendation models and event‑driven scoring to support always‑on campaigns and contextual offers. Support AI‑enabled marketing and digital sales optimization, including audience segmentation, campaign optimization, attribution/uplift measurement, personalized messaging, and digital funnel conversion analytics. Develop call‑center efficiency use cases including call volume forecasting, workforce optimization inputs, routing/prioritization analytics, and AI‑assisted service journeys. Design and implement conversational AI and Gen AI solutions (e.g., virtual assistants, agent‑assist, summarization, knowledge retrieval) to improve customer experience and operational efficiency while adhering to governance controls. Command on PL/SQL with feature extraction, pre‑processing of data, training, scoring, and actionable insight extraction in leading database platforms e.g., Teradata, Oracle etc. Build insights through statistical measures/algorithms/graphs/info graphics and communicate results in a business friendly format for stakeholders and leadership. Evaluate models and establish robust tracking mechanisms for adoption and realized business outcomes; proactively follow up on lead utilization with use case owners. Ensure Responsible AI practices: model governance, documentation, privacy‑by‑design, bias/robustness testing, explainability where required, and secure handling of customer data. Manage and continuously improve the Data Science Operational Framework, including standards for reusable code, model registries, versioning, and release management in collaboration with Technology teams. Work as part of a shared function across organization on use cases to support product growth, cost optimization, customer engagement and customer experience improvements. Minimum Entry QualificationsBachelor’s Degree in Computer Science, Engineering or Similar
Minimum Experience, Essential Knowledge & Skills10 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 timeseries forecasting. Experience implementing hyper‑personalization and Next Best Offer/Action frameworks in marketing and digital sales contexts. Practical knowledge of Gen AI/LLM solutions (e.g., prompt engineering, retrieval‑augmentedgeneration, evaluation methodologies, and model guardrails) and hands‑on experience using Gemini and/or Azure AI for automation and customer experience use cases, Strong command of SQL and PL/SQL for advanced feature extraction with large‑scale data on enterprise data platforms (e.g., Teradata, Oracle, Big Query) Familiarity with MLOps practices – such as version control, pipeline orchestration, model registry, CI/CD deployment patterns, monitoring, and lifecycle management – within Dataiku and incollaboration with Technology teams. Understanding of Responsible AI principles, including privacy, security, and bias mitigation for AI models and automated decisioning. Excellent understanding of telecommunications commercial practices and customer journeys, andthe ability to work cross‑functionally with Product, Marketing, Sales, Digital, Technology teams. #J-18808-Ljbffr
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