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Key Performance Areas & Responsibilities
Data Modelling & ML Development
- Design and build statistical and machine learning models (e.g., clustering/K-Means, XGBoost, CatBoost, LightGBM) to address business problems across segmentation, fraud, retention and churn.
- Translate ambiguous business questions into clearly scoped, testable data science approaches.
- Conduct feature engineering, model tuning, validation and performance benchmarking.
- Ensure segments and model outputs are business-interpretable and have real operational substance.
Data Analysis & Insight Generation
- Perform advanced analysis (e.g., cluster profiling, cohort analysis, penetration/lift analysis) at scale using Spark/Databricks on large datasets (10M+ rows).
- Generate business-friendly insights and recommendations from model outputs for stakeholders.
- Query and manipulate data using SQL (Hive/Trino) and Python (pandas, PySpark).
MLOps & Productionisation
- Support the transition of models from development into production, including monitoring, regression testing and performance evidence.
- Apply MLOps best practices (version control, experiment tracking, CI/CD, model monitoring) in partnership with the AI Ops function.
- Track and report on the performance and drift of models deployed in production.
Business Partnering & Adoption
- Engage directly with business sponsors (CVM, CEX, RAFM, Postpaid/Prepaid, Network) to define requirements and embed AI outputs into workflows.
- Present findings and recommendations to both technical and executive audiences, including DCEO-level reporting.
- Support commercial roadshows, working groups and co-creation sessions to drive adoption of DACoE solutions.
Collaboration & Capability Building
- Collaborate with data engineers, MLOps engineers and BI colleagues to build robust, reusable pipelines.
- Support upskilling initiatives (MLOps, coding standards, Agile/Scrum for data science) and mentor graduates/interns.
Job Specifications: Minimum & Preferred Requirements
Education
- Minimum 3-year tertiary degree in a STEM field: Computer Science, Engineering, Mathematics, Statistics, Data Science or a related quantitative discipline.
- Honours or Master's degree in Data Science, Statistics or a related field advantageous.
- Fluent in English.
Experience
- 2–5 years' experience in a data science/advanced analytics environment (level dependent), with practical exposure to model development, interpretation and deployment.
- Demonstrated experience with classification, regression, clustering and anomaly-detection techniques.
- Experience working with large-scale distributed data processing (Spark/Databricks) is highly advantageous.
- Exposure to MLOps practices and deploying models into production is beneficial.
Technical Skills
Programming & Query Languages
- Python (pandas, PySpark)
- SQL (Hive, Trino)
- Scala/R advantageous
Big Data & ML Platforms
- Databricks
Machine Learning
- K-Means/clustering
- XGBoost
- CatBoost
- LightGBM
- Classification
- Regression
- Anomaly detection
MLOps & DevOps
- MLflow
- CI/CD pipelines
- Azure DevOps
- Model monitoring and drift detection
Visualisation & Reporting
- Power BI
- Databricks
Behavioral & & Core Competencies
- Strategic Thinking & Problem Solving: Able to translate ambiguous business problems into structured analytical approaches.
- Analytical & Innovative: Rigorous, curious, and constantly looking for better ways to model and interpret data.
- Communication & Business Storytelling: Able to explain technical concepts and results in clear, business-friendly language for both technical and executive audiences.
- Cross-Functional Collaboration: Comfortable partnering with business, engineering and governance stakeholders.
- Attention to Detail & Ownership: Takes accountability for code quality, model accuracy and documentation.
- Adaptability & Resilience: Thrives in a fast-paced environment spanning multiple concurrent projects and priorities.