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.
- 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.