Data Scientist

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-TechCo 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 frame open-ended MTNIrancell 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 homogenous 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 explain ability 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 draw conclusions in the analysis/research around data-scientific opportunities, sales retention opportunities, market demographics, and uses-cases to achieve company strategies such as Tel-co to Tech-co transition, utilizing internal/external market intelligence.
• To deploy scientific approaches in order to validate built models by quantifying the resulting accuracy.
• To analyze big data and work with related platforms in order 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 for monetization use cases.

Education:

• B.Sc. in Engineering, Operations Research, Soft Computing, Computer Science, Analytics, Mathematics, Computational Finance or Statistics.

Experience:

• Minimum of 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 & Presentation
• Forecasting
• Statistical Analysis
• Data Science

Behavioral Competencies:

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