Job Brief:

Responsible for transforming data into valuable insights that drive strategic decisions, illuminating our understanding of our customer base, supporting our customer value extraction units, and playing a pivotal role in driving growth.

Key Responsibilities:

  • Identify relevant sources for data collection purposes.
  • Collect, clean, and preprocess data to ensure data quality and integrity.
  • Design, develop and deploy end-to-end data solutions involving, data ingestion, transformation and visualization, including data pipelines, implementing APIs and creating interactive dashboards for stakeholders
  • Apply statistical and machine learning techniques on customer data to extract meaningful insights relating to customer behavior and trends to drive marketing decision-making and strategies
  • Apply machine learning techniques on behavioral, demographic, geographic and psychographic data for customer segmentation, and use these segments to inform personalized marketing strategies
  • Develop and validate prediction models for telecom-related applications
  • Communicate complex data insights to non-technical stakeholders through visualizations and reports effectively
  • Work closely with cross-functional teams to understand business requirements, address data-related challenges and provide data-driven solutions
  • Identify opportunities for process automation and optimization within the Customer Base Management unit operations and leveraging data-driven techniques to streamline workflows and improve efficiency
  • Stay up-to-date with the latest advancements in data science, machine learning and current technologies
  • Identify and explore solutions to enhance the Customer Base Management unit’s analytical capabilities.



Bachelor’s degree in data science, Computer Science, Statistics, Mathematics or any other related field.

Level of Experience:

Limited Experience in a related field

Certifications & Licensure:


  • Data science certification
  • Python for data science
  • SQL for data analysis
  • Data visualization certification
  • Data analytics certification

Tools & Systems:

  • SQL proficiency
  • Python proficiency
  • ML libraries and frameworks (scikit-learn, TensorFlow, PyTorch)
  • Data visualization tools (Matplotlib, ggplot, Superset, Tableau, Power BI)

Technical Skills & Knowledge:

  • Extensive experience with common data science toolkits including Python, R, SQL
  • Proficiency in using machine learning libraries and frameworks (scikit-learn, TensorFlow, PyTorch)
  • Knowledge of a variety of ML techniques (clustering, decision tree leaning, artificial neural networks, etc.)
  • Experience with creating data architectures, data modeling and data mining.
  • Excellent applied statistical skills, such as distributions, statistical testing, regression.
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