Machine Learning Engineer ( Internal/External)

At Safaricom Ethiopia, we are a purpose-led technology company dedicated to transforming lives through digital connectivity and inclusive financial services. In less than five years, we have grown to serve over 15 million 90 days active customers, with our network now reaching 60% of Ethiopia’s population; a testament to our bold vision and trusted partnerships.

Guided by our core values of Customer Obsession, Innovation, Integrity, and Get It Done Together, we are building a dynamic, inclusive, and empowering workplace. We believe our greatest strength lies in our people. That is why, for three consecutive years, we have proudly earned the Top Employer Award. In addition, we have received the African Top Employer recognition for the first time, reflecting our unwavering commitment to fostering a supportive, innovative, and inclusive work environment.

Join Safaricom Ethiopia and be part of a team shaping the digital future of Ethiopia. Here, your work has meaning, your voice matters, and your growth is our priority. Together, we are transforming lives for a digital future.

Role purpose:

The role holder is responsible for providing hands-on technical support for the end-to-end development and operation of the company’s Credit Scoring capability.

Reporting to the Credit Scoring Manager, the role prepares, processes and analyzes GSM, M-PESA, repayment and external data required for credit scoring, and supports the development, testing, deployment and monitoring of machine-learning and analytical models.

The role works closely with Data, Product, Portfolio and Technology teams to ensure that reliable and production-ready data and models are available to support automated credit decisioning and continuous improvement of Credit Products.

Key accountabilities and decision ownership:

Data Preparation & Engineering

• Extract, transform and load data required for Credit Scoring from GSM, M-PESA, repayment, customer and external data sources.

• Clean, structure and prepare datasets for model development, validation and production use.

• Develop and maintain data pipelines supporting Credit Scoring and decisioning.

• Perform data quality checks and identify missing, inconsistent or anomalous data.

• Support integration and preparation of new internal and external data sources.

Credit Scoring & Machine Learning

• Develop and test features used for credit scoring, customer segmentation and risk assessment.

• Support development, training, validation and back-testing of machine-learning and statistical models.

• Analyze customer behaviour, repayment patterns and portfolio performance to identify predictive variables.

• Support model tuning, performance evaluation and comparison of alternative modelling approaches.

• Implement scoring logic and models into production environments in coordination with relevant Technology teams

Model Operations & Monitoring

• Support deployment, version control and ongoing operation of Credit Scoring models.

• Monitor model performance, data drift and changes in customer or portfolio behaviour.

• Support model retraining and enhancement based on new data and actual repayment outcomes.

• Investigate model or data issues affecting scoring accuracy or credit decisioning.

• Maintain appropriate technical documentation for datasets, features, models and pipelines.

Analytics & Business Support

• Provide analytical support to the Credit Scoring Manager and Product & Portfolio teams.

• Conduct analysis on approval rates, repayment performance, customer segments and credit risk indicators.

• Support testing of changes to eligibility rules, credit limits, decision rules and scoring strategies.

• Translate analytical findings into actionable inputs for Credit Product and portfolio optimization.

Key performance indicator:

• Timeliness and quality of Credit Scoring datasets and data pipelines.
• Accuracy and reliability of data preparation and feature engineering.
• Model development and deployment cycle time.
• Stability and performance of production scoring models.
• Effectiveness of model monitoring and issue resolution.
• Successful integration of new internal and external data sources.
• Automation of data preparation, scoring and model monitoring processes.

Core competencies, knowledge and experience:

• 2 - 4 years of experience in Machine Learning, Data Science, Data Engineering, Analytics or related technical roles.

• Hands-on experience preparing large datasets for analytical and machine-learning use.

• Strong understanding of supervised learning, classification models and model evaluation techniques.

• Experience with feature engineering, model validation and performance monitoring.

• Strong SQL and Python skills.

• Experience with ETL, data pipelines and data processing frameworks.

• Ability to work with transactional, behavioral and time-series data.

• Strong analytical and problem-solving skills.

• Ability to work effectively with both technical and business stakeholders.

Must have technical / professional qualifications:

• Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering or related field.
• Strong programming skills in Python and SQL.
• Experience with machine-learning libraries and analytical tools.
• Experience working with relational databases, data warehouses or data lake environments.
• Understanding of model development, validation and production deployment.
• Good written and verbal communication skills in English.

Desired

• Experience in Credit Scoring, Digital Lending, Banking, FinTech, Telecommunications or Mobile Financial Services.
• Experience working with GSM, mobile wallet, transactional or alternative data.
• Experience with cloud-based data and machine-learning environments.
• Experience with MLOps, model monitoring and automated deployment pipelines.
• Understanding of credit risk concepts such as default, delinquency, NPLs and repayment behaviour.

How to Apply. If you feel that you are up to the challenge and possess the necessary qualification and experience, kindly proceed to create/ update your candidate profile on the recruitment portal and then Click on the apply button. Remember to attach your resume.