Data Scientist & AI Developer – ICT

About the role

We are looking for a Data Scientist and AI Developer to lead data engineering and AI initiatives across Britam. The role will design scalable pipelines, develop machine‑learning models and ensure data solutions support business performance, automation and analytics.

Key responsibilities

• Design and maintain scalable data pipelines using Python, SQL, Spark and Microsoft Fabric.
• Oversee data ingestion, transformation, quality and ensure reliability of reporting and ML‑ready datasets.
• Lead development and deployment of machine‑learning use cases such as claims risk, lapse prediction and fraud detection.
• Convert models into APIs and integrate them with business systems, monitoring performance and recalibrating as needed.
• Review and approve junior team code and pipeline configurations.
• Guide data stewards in data preparation and experimentation.
• Develop and maintain dashboards in Power BI, aligning them with business needs.
• Provide predictive analysis support to Finance, Actuarial, Risk and Product teams and validate insights before release.
• Drive automation of recurring reporting and manual workflows.
• Ensure compliance with data security, privacy and ethical AI standards, partnering with Risk & Compliance.
• Maintain documentation for audit and regulatory purposes.

Required profile

• University degree in actuarial science, statistics, engineering, mathematics, physics, economics or a related quantitative field.
• 2–4 years of experience in a similar data‑science or AI development role.
• Proven experience building and applying advanced machine‑learning algorithms (regression, clustering, decision trees, simulation, scenario analysis).
• Ability to troubleshoot complex technical issues and deliver scalable, high‑performance solutions.
• Strong coding practices with clean, maintainable and well‑documented code.
• Good interpersonal skills and experience working with cross‑functional teams.

Required skills

• Python
• SQL
• Apache Spark
• Microsoft Fabric
• Power BI
• SAS
• Visual Basic
• Machine‑learning libraries and model deployment (e.g., APIs)