ICT Data Scientist & AI Developer

Role Overview

This position sits where data infrastructure meets applied machine learning: you will build and run the pipelines that feed reporting, analytics and models, then carry those models the last mile into production APIs and everyday business workflows. Your week splits between hands-on engineering, reviewing the output of a small team of data professionals, and turning requests from finance, actuarial, risk and product colleagues into reliable, well-documented data products. The role carries real weight because underwriting, claims and retention decisions across the organisation depend on data people can trust and models that keep performing long after launch.

Key Responsibilities

• Design, build and maintain data pipelines in Python, SQL, Spark and Microsoft Fabric, covering ingestion, transformation and the data-quality checks that keep datasets dependable.
• Keep reporting datasets and machine-learning training data accurate, performant and fit for repeated reuse across analytical and operational teams.
• Extend and curate the master data management platform so core entity data stays consistent, well-governed and reusable across functions.
• Review and approve code, pipeline configurations and design decisions from junior engineers, and coach data stewards on data preparation and experimentation.
• Take machine-learning use cases — claims risk, lapse prediction, fraud detection and similar — from prototype to production, exposing models as APIs and integrating them into business systems.
• Monitor deployed models for drift and degradation, and recommend retraining or recalibration when performance slips below acceptable levels.
• Oversee dashboard and reporting development in Power BI or a comparable tool, validating insights with finance, actuarial, risk and product partners before they reach stakeholders.
• Automate recurring reporting and manual workflows, and work with risk and compliance colleagues to uphold data security, privacy, ethical-AI standards and audit-ready documentation.

Requirements & Qualifications

• University degree in actuarial science, statistics, engineering, mathematics, physics, economics or another quantitatively demanding discipline.
• 2–4 years of experience in a comparable data engineering, data science or analytics delivery role.
• Proven ability to create and apply advanced machine-learning and statistical techniques, including regression, clustering, decision trees, simulation and scenario analysis.
• Demonstrated expertise with machine-learning algorithms and Microsoft Fabric, alongside comfort working across Python, SQL and Spark.
• Working knowledge of statistical packages or programming tools such as SAS or Visual Basic, plus a solid grounding in database management systems.
• Familiarity with the Kenyan insurance market, including how reinsurance arrangements and structures shape data and risk reporting.
• A track record of writing clean, maintainable, well-documented code with attention to application quality, performance and security.
• Strong diagnostic skills for resolving complex technical issues, and the interpersonal ability to collaborate closely with product managers, designers, engineers and business stakeholders.