Data Scientist

Job Description

Mission:

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

Roles & Responsibilities:

• Liaise with various marketing/commercial teams to induct them of data science concepts to address business requirements.
• Collaborate with business partners to specify use cases and work with end users for agile use case improvement.
• Collaborate with subject matter experts to select the relevant sources of information and translate the business requirements into a data-scientific project.
• Work in cross-functional agile/scrum teams alongside product, network, and customer care to co-create and implement solutions aligned with business needs.
• Contribute to conducting undirected research and framing open-ended MTN Irancell business questions.
• Collaborate with the data governance team to ensure that the correct standards align with the data pipeline development process.
• Process huge volumes of data collected from multiple internal and external sources.
• Search and propose various ways to compensate for unavailable required data by conducting scientific experiments.
• Develop models and frames of business scenarios that are meaningful and affect critical business processes and/or decisions.
• Build high-performance algorithms, prototypes, predictive models, and proof of concepts to review data from various perspectives and generate insightful reports.
• Refine and tune data models to be able to respond to various homogeneous data structures.
• Exploit multiple new/existing algorithms for processing data to generate real business value.
• Discover insights and identify opportunities using statistical, algorithmic, machine learning, data mining, and visualization techniques.
• Propose and design data-efficient models that are smartly developed in accordance with the quality of existing data.
• Employ sophisticated analytics programs, machine learning, and statistical methods to do both predictive and prescriptive modeling.
• Implement MLOps practices, including model versioning, monitoring, and CI/CD deployment pipelines for the respective Use cases.
• Leverage advanced machine learning algorithms and models where appropriate.
• Integrate model explainability techniques such as SHAP and LIME to ensure transparent decision-making.
• 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.
• 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.
• 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.
• Deploy scientific approaches to validate built models by quantifying the resulting accuracy.
• 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.
• Create and maintain interactive dashboards for visualizing campaign and offer performance.
• 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.