Lead execution of data engineering and AI initiatives across the organization; Responsible for delivery quality, technical guidance, and ensuring data solutions support business performance, automation, and analytics objectives.
Key Responsibilities:
Design and maintain scalable data pipelines (Python, SQL, Spark, Microsoft Fabric).
Oversee ingestion, transformation, and data quality processes.
Ensure reliability and performance of reporting datasets and ML-ready data.
Support optimization and value extraction from Britam's MDM platform.
Review and approve junior team code and pipeline configurations.
Lead development and deployment of ML use cases (e.g., claims risk, lapse prediction, fraud detection).
Guide the data stewards in data preparation and experimentation.
Convert ML models into APIs and support integration into business systems.
Monitor model performance and recommend recalibration when needed.
Oversee dashboard development (Power BI or similar) and ensure business alignment.
Support Finance, Actuarial, Risk, and Product teams with predictive analysis.
Validate insights before release to business stakeholders.
Drive automation of recurring reporting and manual workflows.
Ensure compliance with data security, privacy, and ethical AI standards.
Partner with Risk & Compliance on data protection implementation.
Maintain documentation for audit and regulatory purposes.
Knowledge, Experience and Qualifications required and Essential Competencies - External
University Degree in actuarial science, statistics, engineering, mathematics, physics, economics or related field with strong emphasis on mathematical proficiency
2-4 years' experience in a similar position
Experience in creating and using advanced machine learning algorithms and statistics: regression, simulation, scenario analysis, modelling, clustering, decision trees, etc.
Ability to identify, analyze, and resolve complex technical issues, ensuring optimal performance, scalability, and user experience.
Dedication to writing clean, maintainable, and well-documented code with a focus on application quality, performance, and security.
Demonstrated interpersonal skills and ability to work closely with cross-functional teams, including product managers, designers, and other engineers.
Demonstrated expertise machine learning algorithms and Microsoft fabric
Technical/ Functional competencies
Statistical techniques
Proficient knowledge on the insurance industry in Kenya
Knowledge of reinsurance arrangements and structures
Knowledge of statistical packages or programming (e.g. SAS, Visual Basic)