Senior AI/ML Specialist at Leadway Assurance Company

Role Overview

• We are a leading insurance organization developing next-generation line of business applications for General, Life and Health Insurance.
• As the Senior AI/ML specialist, you will design and deploy AI/ML models focused on use cases such as underwriting risk, fraud detection, and advanced analytics within a multi-tenant SaaS platform.
• Collaborating with the Data Engineer, you will build end-to-end pipelines—from data ingestion to model deployment—supporting real-time dashboards and analytics for multiple insurance clients.

Key Responsibilities

• Model Development: Research, build, and optimize ML models for underwriting risk evaluation, claims trends fraud detection and other use cases as communicated.
• Data Collaboration: Work with the Data Engineer to ensure high-quality, partitioned data pipelines for multi-tenant analytics.
• Integration & Deployment: Implement models into microservices architecture (in .NET/Node.js), ensuring minimal latency and scalability.
• Regulatory Alignment: Incorporate IFRS 17, NDPR, and NIST security considerations into AI/ML solutions.
• Continuous Optimization: Monitor model performance post-deployment, iterating to improve accuracy and speed.

Technical Requirements

• High proficiency in Semantic Kernel, and ML.NET
• Intermediate proficiency in Azure AI Foundry Senior UX Designer – Core Insurance System (SaaS)

Qualifications

• Master’s degree in data science, Computer Science, or related field (or equivalent experience).
• Expertise in Python ML frameworks (TensorFlow, PyTorch, scikit-learn) and cloud-based ML pipelines.

Experience

• 5+ years in enterprise AI/ML roles, delivering production-grade solutions.
• Familiarity with insurance or financial data modeling is highly advantageous.

Other Skills & Competencies

• Strong problem-solving and statistical analysis skills.
• Collaborative approach to working in cross-functional sprints.
• Effective communication to explain complex ML concepts to non-technical stakeholders.
• On-site availability and willingness to align with sprint cycles.