We’re an award-winning global communications company operating in nine countries across the Middle East, North Africa, and Southeast Asia. Our strategy is to become the region’s leading digital infrastructure provider. Ooredoo Group’s strategic vision is guided by five key pillars:
Value-Focused Portfolio: Boosting asset returns by focusing on telco operations, towers, data centres, the sea cable business and fintech.
Strengthen the Core: Optimally using deployed capital and maintain an appropriate cost structure.
Evolve the Core: Monetising opportunities to generate new revenue streams via programmes focusing on analytics, digitalisation of operations, and partnerships with digital service providers.
People: Building an engaged and empowered workforce through integrated learning programs and coaching and mentoring.
Excellence in Customer Experience: Creating superior customer experiences.
From day one, every employee who joins our team becomes an integral part of our success journey. We offer you the chance to enhance your skills, advance your career, and maintain a healthy work-life balance. Empowering you to catapult your personal and professional growth. If you’re looking to challenge your growth potential, Ooredoo is the employer for you.
The Data Science will analyze large and complex datasets to extract actionable insights and build predictive models that drive business value. This role is crucial in creating AI solutions that support operational excellence (e.g., predictive maintenance, network fault diagnostics). The Data Scientist will work closely with AI Engineers, Data Engineers, and Business Analysts to design, test, and deploy AI models that address the Group’s needs across OpCos.
Data Exploration and Analysis: Analyze complex datasets to uncover patterns, trends, and relationships that can drive decision-making and provide insights into business problems.
Predictive Modeling: Design and develop machine learning models to forecast network traffic, identify anomalies in the network, IT or customer complaints, and improve operational efficiencies across the Group.
Feature Engineering: Collaborate with Data Engineers to extract, transform, and create relevant features from raw data for use in AI models.
Model Testing and Evaluation: Use statistical methods to validate and evaluate the performance of machine learning models. Continuously refine models to improve accuracy and business impact.
Collaboration: Work with AI Engineers, Business Analysts, and stakeholders across HQ and OpCos to ensure that data models align with business requirements and objectives.
Data-driven Decision Making: Provide data-driven recommendations to business units by translating complex data analyses into actionable insights.
Algorithm Development: Develop and refine machine learning algorithms that are applicable to a variety of business use cases, such as traffic predictions, anomaly detection, root cause analysis, and operational diagnostics.
Visualization: Create clear and informative visualizations that communicate data findings and insights to non-technical stakeholders.
Research: Stay updated on the latest data science techniques and tools. Proactively propose new methodologies and approaches that could benefit the Group's AI initiatives..
4+ years of experience in data science roles, with hands-on experience in machine learning and statistical modeling.
Proficiency in Python and data science libraries such as pandas, scikit-learn, and TensorFlow/PyTorch.
Strong knowledge of statistics and data analysis methods (e.g., regression, classification, clustering).
Experience working with large datasets and relational databases (e.g., SQL, NoSQL).
Bachelor’s degree in data science, Statistics, Computer Science, or related field (Master’s degree preferred).
Experience in telecommunications or mobile network industries.
Knowledge of deep learning, natural language processing (NLP), or advanced machine learning techniques.
Experience with cloud-based data science tools and environments (e.g., AWS, Google Cloud, Azure).
Familiarity with A/B testing and experimental design for evaluating model performance.