Manager – Data Science, Advanced Analytics & AI

Department: Fintech

Reports to: Senior Manager BI

Location: Kigali, Rwanda

Posted: Oct 14, 2026

Deadline: Oct 21, 2026

Status: Open

About MTN

Mobile Money Rwanda Limited is committed to attracting, developing and retaining exceptional talent that contributes to our mission of making our customers' lives a whole lot BRIGHTER.

We foster an inclusive, innovative and technology-driven culture that empowers our people to challenge the status quo, embrace emerging technologies and deliver sustainable business value.

We are therefore inviting applications from highly motivated, innovative and results-oriented professionals for the position below.

Mission/Core purpose of the Job

The Manager – Data Science, Advanced Analytics & AI will be responsible for establishing, building and leading the Data Science function at Mobile Money Rwanda as a strategic capability that accelerates the execution of the MoMo Strategy 2030

Reporting to the Head of Business Intelligence, Customer Value Management & Pricing, the incumbent will define the Data Science vision, operating model, governance framework and technology roadmap while building and leading a high-performing Data Science team that leverages Artificial Intelligence (AI), Machine Learning (ML), Generative AI (GenAI) and advanced analytics to solve business challenges and create sustainable competitive advantage.

The role will lead the identification, prioritization, design, development and deployment of enterprise AI initiatives and use cases across the Mobile Money ecosystem. The incumbent will partner with business and technology leaders to embed AI into products, customer journeys and business processes, delivering measurable improvements in customer experience, financial inclusion, operational efficiency, revenue growth, risk management and intelligent decision-making.

As the leader of the Data Science capability, the incumbent will establish best practices, AI governance, MLOps capabilities and technical standards while fostering a culture of innovation, experimentation and data-driven decision-making across Mobile Money Rwanda.

Key Performance Areas

1. Data Science Leadership & Strategy

• Establish, build and lead the Mobile Money Data Science function as a strategic business capability.
• Develop the vision, operating model, governance framework, standards and best practices for Data Science across Mobile Money.
• Recruit, mentor and lead a high-performing Data Science team capable of delivering enterprise AI solutions.
• Develop and execute the Data Science roadmap aligned to the Mobile Money Strategy and AI Strategy 2030.
• Establish a Data Science Centre of Excellence to drive AI innovation, collaboration and knowledge sharing across the organization.
• Promote a culture of experimentation, innovation and data-driven decision-making.
• Continuously evaluate emerging AI technologies and recommend strategic opportunities that enhance Mobile Money's competitive advantage.

2. AI Strategy & Business Transformation

• Lead the implementation and continuous evolution of the Mobile Money AI Strategy 2030.
• Identify, evaluate and prioritize high-impact AI initiatives that create measurable commercial and operational value.
• Translate business challenges into scalable AI and advanced analytics solutions.
• Partner with executive leadership to embed AI into strategic planning and business decision-making.
• Drive enterprise-wide AI adoption across Commercial, Product, Technology, Operations, Risk, Finance and Customer Experience functions.
• Develop business cases and value realization frameworks for AI investments.

3. AI Product Development & Enterprise Use Cases

Lead the design, development and deployment of AI solutions across strategic business domains including:

• Digital Lending & Credit Risk
• Customer Value Management
• Customer Segmentation
• Customer Lifetime Value
• Personalization & Next Best Offer
• Churn Prediction & Retention
• Fraud Detection & Prevention
• AML & Transaction Monitoring
• Merchant Intelligence
• Revenue & Pricing Optimization
• Customer Experience Analytics
• Collections Optimization
• Financial Inclusion
• Intelligent Process Automation
• AI-powered Customer Support
• Generative AI & Digital Assistants
• Executive Decision Intelligence
• Emerging strategic AI initiatives aligned with business priorities.

4. Digital Lending & Credit Analytics

• Develop, validate and continuously enhance customer credit scorecards and risk models to support Mobile Money's digital lending products.
• Design and implement predictive models for Non-Performing Loan (NPL) forecasting, early warning indicators and portfolio risk monitoring.
• Assess, benchmark and continuously monitor the performance of internal and third-party partner scorecards, recommending recalibration and optimization to improve portfolio quality and business performance.
• Develop Machine Learning models to identify opportunities, emerging risks and performance gaps within the lending portfolio and recommend optimization strategies.
• Develop customer segmentation and propensity models to improve credit eligibility, loan limit assignment, pricing strategies and responsible lending.
• Perform portfolio analytics to evaluate loan performance, repayment behaviour, customer risk profiles and product profitability.
• Partner with Product, Risk and Commercial teams to optimize lending policies and customer journeys through data-driven insights.
• Monitor production scorecards and machine learning models, ensuring periodic validation, retraining and continuous performance improvement.
• Evaluate alternative data sources to strengthen credit decisioning and promote financial inclusion.
• Develop dashboards and executive reporting that monitor portfolio health, scorecard performance, NPL trends and lending KPIs.

5. Machine Learning & Advanced Analytics

• Design, build, validate and deploy predictive, prescriptive and Generative AI models.
• Develop scalable AI solutions using transactional, KYC, GSM, behavioural, merchant and external datasets.
• Build forecasting, recommendation, optimization and anomaly detection models.
• Develop explainable AI models that support transparent and responsible decision-making.
• Continuously monitor, validate and retrain production models to maintain optimal business performance.
• Conduct hypothesis testing, experimentation and statistical analysis to identify growth opportunities.

6. AI Platform, MLOps & Engineering

• Lead the implementation and operationalization of enterprise AI and Machine Learning platforms.
• Design and manage end-to-end MLOps capabilities including experimentation, deployment, monitoring, retraining and governance.
• Collaborate with Data Engineering teams to build scalable data pipelines, feature stores and AI infrastructure.
• Ensure AI platforms are secure, resilient, scalable and cost-efficient.
• Oversee deployment of AI services leveraging relevant advanced technologies

7. Business Intelligence & Decision Science

• Deliver advanced analytics and strategic insights that influence executive decision-making.
• Design executive dashboards and automated reporting solutions.
• Translate analytical findings into actionable business recommendations.
• Present analytical insights and AI strategies to Executive Management and senior stakeholders.
• Develop performance measurement frameworks that quantify the value delivered by AI initiatives.

8. AI Governance & Responsible AI

• Establish AI governance standards, model validation processes and responsible AI principles.
• Ensure compliance with banking regulations, data privacy, cybersecurity and internal governance requirements.
• Define standards for model explainability, fairness, transparency and ethical AI.
• Implement monitoring frameworks to ensure AI solutions remain compliant, reliable and auditable.

9. Stakeholder Management & Collaboration

• Partner with Product, Commercial, Technology, Risk, Finance, Compliance and Operations teams to deliver AI-enabled business outcomes.
• Collaborate with MTN Group, technology vendors, fintech partners and academic institutions to accelerate AI innovation.
• Translate complex technical concepts into business language for executive stakeholders.
• Promote AI literacy and capability development across the organization.
• Mentor Data Scientists and Analytics professionals while fostering collaboration across teams. 

Experience

• Minimum 5 years' experience in Data Science, Artificial Intelligence or Machine Learning.
• At least 3 years' experience within FinTech, Digital Financial Services, Banking or Telecommunications.
• Proven experience establishing or leading Data Science or AI capabilities.
• Demonstrated experience building and deploying production AI/ML solutions.
• Experience implementing enterprise MLOps platforms and AI governance frameworks.
• Experience building predictive models, scorecards and optimization models that deliver measurable business value.
• Experience leading cross-functional AI initiatives from concept to deployment.
• Experience managing or mentoring technical teams.
• Experience working within Agile and DevOps environments.
• Experience engaging senior business stakeholders and influencing strategic decisions.

Technical Competencies

• Advanced Python (Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch, XGBoost, LightGBM).
• Experience with Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), LangChain, Hugging Face and AI APIs.
• Strong expertise in MLflow, Kubeflow, SageMaker, Vertex AI or Azure ML.
• SQL and relational/non-relational databases (PostgreSQL, Oracle, SQL Server, MongoDB).
• Apache Spark, Hadoop and distributed computing frameworks.
• Docker, Kubernetes, APIs, microservices and CI/CD.
• Cloud platforms (Azure, AWS or Google Cloud).
• Power BI, Tableau or similar visualization tools.
• Statistical modelling, optimization techniques and experimentation frameworks.

Core Competencies

• Strategic leadership
• AI innovation and technology foresight
• Commercial and business acumen
• Data-driven decision-making
• Customer-centric mindset
• Strong analytical and problem-solving skills
• Executive communication and influencing
• Stakeholder management
• Team leadership and coaching
• Cross-functional collaboration
• Results orientation
• Continuous learning and adaptability
• Ethical and responsible AI mindset

Education Background

Minimum Requirements

• Bachelor's Degree in Computer Science, Data Science, Artificial Intelligence, Statistics, Mathematics, Engineering or another quantitative discipline.

Preferred

• Master's Degree in Artificial Intelligence, Data Science, Computer Science, Statistics, Business Analytics or a related field.
• MBA is an added advantage.
• Professional certifications in Artificial Intelligence, Machine Learning, Cloud Computing, Data Engineering or MLOps are advantageous. 

How To Apply

All interested candidates are requested to apply through the MTN Job portal and submit their updated curriculum vitae with copies of notified academic credentials no later than 21^st October 2026. MTN Job portal: https://www.mtn.co.rw/careers/  

We strongly encourage applications from women or individuals with disabilities.