Full-Time Senior Data Scientist

The Insurance AI team conducts technical work to design, develop and support products that use Nearmap and third-party data to derive insurance risk insights. The Senior Data Scientist plays a pivotal role in designing, developing, and operationalizing advanced risk models that leverage Nearmap’s AI data and third-party sources to deliver actionable insurance risk insights. This role bridges cutting-edge technical model development with actuarial applications, helping insurance companies integrate Nearmap’s AI-driven intelligence into pricing, underwriting, modeling, and regulatory workflows.

As a senior member of the Insurance AI team, you will:

• Lead modeling initiatives from conception through deployment.
• Serve as a trusted technical and domain expert for both internal teams and external clients.
• Drive innovation in how Nearmap’s data is applied within insurance products and workflows.
• Provide strategic actuarial and statistical guidance to ensure models meet both technical rigor and practical business needs.

This role requires the ability to seamlessly transition between in-depth technical work, high-level solution design, and client-facing consulting with senior insurance stakeholders. As such, deep experience working as a data scientist or actuary training and evaluating models in the property casualty (P&C) insurance space is critical.

Key Responsibilities

1. Technical Leadership in Model Development
   • Lead the design, development, and refinement of insurance risk models using Nearmap AI data and other sources.
   • Conduct exploratory data analysis, advanced feature engineering, model selection, training, validation, and performance optimization.
   • Guide the integration of geospatial, property, and external datasets into scalable, production-ready solutions.
2. Actuarial & Regulatory Expertise
   • Serve as a senior technical liaison to actuarial, data science, and pricing teams at insurance companies, ensuring successful adoption of Nearmap data in their workflows.
   • Advise on and prepare regulatory filing materials, respond to objections, and ensure models meet compliance standards for property/casualty insurance.
3. Strategic Data Asset Development
   • Spearhead efforts to grow and optimize Nearmap’s proprietary policy and loss database for product and modeling innovation.
4. Client Engagement & Solution Integration
   • Lead high-impact retro tests, validation studies, and custom analyses for top-tier insurance clients.
   • Translate complex technical results into strategic business recommendations for senior decision-makers.
5. Thought Leadership & Mentorship
   • Mentor junior team members on best practices in modeling, data analysis, and insurance-specific applications.

Key Requirements

• 5+ years of practical experience in property/casualty insurance in a data science or actuarial role, with a strong track record of delivering models and analytics in production environments.
• Proven leadership in complex modeling projects from ideation through deployment.
• Experience advising senior insurance stakeholders and influencing strategic decision-making.

Required:

• Domain Knowledge: Deep expertise in property/casualty insurance pricing, rating, and regulatory requirements; mastery of actuarial data and modeling (Tweedie distributed data, GLMs, GBMs) and advanced statistical concepts (e.g., variable selection, overfitting, fairness testing, Gini, lift, AUC, cross-validation, sensitivity analysis).
• Data Science: Advanced capability in feature engineering, model validation, uncertainty quantification, and statistical testing; fluency in SQL for data extraction/manipulation.
• Programming: Expert in Python (NumPy, Pandas, Scikit-learn, Matplotlib) and git-based workflows.
• Communication: Exceptional ability to translate technical findings into actionable business insights for executive-level audiences.
• Data/ML Engineering: Familiarity with scalable pipelines, MLOps practices, and production deployments

Highly Desirable:

• Experience with geospatial and imagery data (GeoPandas or similar).
• Prior experience integrating AI-derived datasets into insurer pricing and underwriting workflows.

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