Mission:

To deliver intelligent and scalable data solutions that adapt to the dynamic landscape of the telecom and technology sectors.To provide full-spectrum, data-driven approaches to address critical challenges in marketing, operations, and customer experience, enabling the Telco-to-Tech-Co transition.To drive innovation through advanced analytics, AI/ML, and Generative AI solutions, including the use of LLMs for automation, personalization, and enhanced decision-making.To unlock business value through monetization of internal data assets and enable data-driven products and partnerships aligned with MTNIrancell’s growth strategy.

Roles & Responsibilities:

To liaise with various marketing/commercial teams to induct them of data science concepts to address business requirements.To collaborate with business partners to specify use cases and work with end users for agile use case improvement.To collaborate with subject matter experts to select the relevant sources of information and translate the business requirements into a data-scientific project.To work in cross-functional agile/scrum teams alongside product, network, and customer care to co-create and implement solutions aligned with business needs.To contribute to conducting undirected research and framing open-ended MTN Irancell business questions.To collaborate with the data governance team to ensure that the correct standards align with the data pipeline development process.To process huge volumes of data collected from multiple internal and external sources.To search and propose various ways to compensate for unavailable required data by conducting scientific experiments.To develop models and frames of business scenarios that are meaningful and affect critical business processes and/or decisions.To build high-performance algorithms, prototypes, predictive models, and proof of concepts to review data from various perspectives and generate insightful reports.To refine and tune data models to be able to respond to various homogeneous data structures.To exploit multiple new/existing algorithms for processing data to generate real business value.To discover insights and identify opportunities using statistical, algorithmic, machine learning, data mining, and visualization techniques.To propose and design data-efficient models that are smartly developed in accordance with the quality of existing data.To employ sophisticated analytics programs, machine learning, and statistical methods to do both predictive and prescriptive modeling.To implement MLOps practices, including model versioning, monitoring, and CI/CD deployment pipelines for the respective Use cases.To leverage advanced machine learning algorithms and models where appropriate.To integrate model explainability techniques such as SHAP and LIME to ensure transparent decision-making.To stay up to date with the latest trends in Generative AI and experiment with use cases such as automated insight summarization, chatbot enhancement, and personalized content delivery.To apply foundational concepts of Large Language Models (LLMs) and prompt engineering for developing and enhancing telecom-focused AI applications such as smart chatbots, virtual assistants, and summarization tools.To apply advanced knowledge and conclude the analysis/research around data-scientific opportunities, sales retention opportunities, market demographics, and use cases to achieve company strategies, such as tel-co to tech-co transition, utilizing internal/external market intelligence.To deploy scientific approaches to validate built models by quantifying the resulting accuracy.To analyze big data and work with related platforms to build data-driven business insights and high-impact data-scientific models to generate significant business value and present them to the management.To create and maintain interactive dashboards for visualizing campaign and offer performance.To develop strong data storytelling and narrative skills to effectively communicate complex insights to non-technical business stakeholders.To build value scoring models, audience insights, and data-as-a-service (DaaS) frameworks formonetization use cases.

Education:

Bachelor's degree in Operations Research, Soft Computing, Computer Science, Analytics, Mathematics, Computational Finance, or Statistics.

Experience:

At least 3 years of experience in an area of specialization (data science/data analysis experience is preferred).Experience working in a medium to large organization.

Technical Competencies:

Reporting and analysis.Data visualization and presentation.Forecasting.Statistical analysis.Data science.

Behavioral Competencies:

Lead with care.Can-do with integrity.Serve with respect.Collaborate with agility.Act with inclusion.