We’re determined to make a difference and are proud to be an insurance company that goes well beyond coverages and policies. Working here means having every opportunity to achieve your goals – and to help others accomplish theirs, too. Join our team as we help shape the future.

• Experience in statistical modelling and machine learning using Python, including extensive use of pandas, NumPy, scikit-learn, and strong SQL for data exploration, feature development, and knowledge preparation; familiarity with PyTorch and/or TensorFlow preferred.
• Experience across the end-to-end modelling lifecycle, including problem framing and requirements gathering, experiment design, offline evaluation, and ongoing production validation and monitoring.
• Solid understanding and practical application of core machine learning methods, with 3+ years of experience applying deep learning architectures in real-world use cases.
• Experience designing and operationalizing model evaluation and monitoring approaches, including test set creation (gold and/or synthetic), metric definition and tracking (e.g., classification, forecasting, ranking/IR, and business KPIs), and supporting A/B testing, drift detection, and performance regression monitoring.
• Experience working with unstructured data, including document parsing and OCR fundamentals, text normalization, metadata and lineage awareness, and PII detection or redaction considerations.
• Experience using Git and Unix-based development environments, with experience building reproducible notebooks or pipelines and ensuring repeatable analytical workflows; 3+ years of exposure to basic container and cloud fundamentals supporting deployment workflows
• Experience communicating modeling decisions, design tradeoffs, evaluation results, and risks to both technical and non-technical audiences, and translating analytical outcomes into measurable business impact.
• Experience working with cloud-based AI platforms such as Google Vertex AI, AWS SageMaker or Bedrock, or Azure AI Services, supporting experimentation, model training, and deployment.
• Experience deploying models and integrating scoring logic into production systems, including operation within complex enterprise or packaged application environments (e.g., Duck Creek, Ratabase).
• Experience with NLP and Generative AI capabilities, including embeddings, retrieval strategies (dense and hybrid), chunking approaches, prompt engineering, structured outputs, and contributing to Retrieval-Augmented Generation (RAG) solutions and evaluations.
• Experience or exposure to advanced GenAI applications and extensions, such as agent or tool-use concepts, domain-specific knowledge graph integration, synthetic data generation, sentiment modelling, and GenAI use cases in filing or compliance contexts.
• Experience working within enterprise AI governance expectations, including aligning model development with compliance, privacy, documentation, and ethical standards.

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