Job Description

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 Data Scientist (Insurance, Risk Modeling) plays a hybrid role that bridges technical model development with actuarial applications, helping insurance companies unlock the value of Nearmap's AI-driven data within their pricing, underwriting, modeling, and regulatory workflows. They will achieve this through direct engagement as well as through optimization of Nearmap’s product suite for the insurance use case.

This role combines two key responsibilities: developing and refining risk models using Nearmap AI data and other sources, and providing actuarial expertise to ensure these models and derived insights are statistically robust, relevant to insurance use cases, and suitable for integration into rating plans, filings, and customer-facing solutions. The role requires someone who can seamlessly transition between technical model development work and client-facing actuarial consultation.

The Data Scientist will contribute to model development efforts including exploratory data analysis, feature engineering, model training, validation, and performance optimization. They will also support insurance company clients through retro tests, model integration guidance, and other analyses that demonstrate value and facilitate adoption. A core aspect of this role will be to build, curate and grow Nearmap's own database of policy and loss data through partnership with insurers.

A portion of the Data Scientist's role will be to liaise directly with actuaries and data scientists at insurance companies to support their testing and integration of Nearmap AI data into their models, workflows and rate filings through retro tests and other ad-hoc analyses. As such, experience working as a data scientist or actuary in the property casualty (P&C) insurance space is critical.

The Data Scientist will also play a key role in developing materials and analyses to demonstrate and quantify the value of these products for insurance customers and assist in integration and with regulatory requirements.

Skills & Experience we are looking for:

• Hybrid Model Development Contribute to the development and refinement of risk models using Nearmap AI data and other sources, including feature engineering, model training, validation, and performance optimization.
• Actuarial Support: Serve as technical liaison to actuaries, data scientists, and pricing teams at insurance companies, supporting their testing, integration, and use of Nearmap data in pricing, underwriting, filings, and retro tests.
• Claims database: Create, curate and grow Nearmap’s internal policy and loss database, and use it to derive property risk insights.
• Regulatory support: Support regulatory activities, including preparing filing materials, responding to regulatory objections, and ensuring accurate documentation and compliance for Nearmap data products.
• Validation: Conduct detailed validation studies, sensitivity analyses, and scenario testing to demonstrate the accuracy, reliability, and business value of Nearmap models across insurance use cases.
• Develop customer-facing materials, presentations, and quantitative analyses to help insurers understand and incorporate Nearmap data into their actuarial and pricing workflows.
• Cultivate deep knowledge of Nearmap’s data and leverage it to support continual improvement of our models

Qualifications

Data Scientist Level: Data Scientist Level, plus formal postgraduate qualifications in data science, statistics or actuarial studies (Masters/PhD, or demonstrated depth of knowledge at that level), plus a minimum of 3 years practical working experience in property/casualty insurance in a data science or actuarial role, and the pragmatic aspects of delivering the project in a way that meets the business goals.

• Mandatory
  • Data Scientist Level, plus formal postgraduate qualifications in data science, statistics, actuarial studies or other relevant field, plus a minimum of 2 years practical working experience in property/casualty insurance in a data science or actuarial role, and the pragmatic aspects of delivering the project in a way that meets the business goals.
  • Domain Knowledge – property/casualty insurance pricing, rating and regulatory requirements: Experience and comfort building insurance pricing models using property data following traditional actuarial methods (e.g., GLMs); familiarity with regulatory requirements for property/casualty insurance rating models and fluency with related statistical concepts (e.g., variable selection, overfitting, fairness testing, gini, lift AUC, cross validation, sensitivity analysis, etc.)
  • Data Science: Strong grasp of data science fundamentals (data analysis, feature engineering, modelling frameworks, model validation, confidence intervals, etc.), and facility at data extraction and manipulation using SQL.
  • Programming/Tech Environments: Ability to code in scientific python using such libraries as NumPy, Pandas, ScikitLearn and Matplotlib, and use git for source control.
  • Communication: Excellent communication skills and experience in client-facing roles, with the ability to translate technical findings into actionable insights for insurance customers.
  • Scientific Approach: Follows the scientific method of formulating hypotheses, and applying statistical tests to validate them.
  • Data / ML Engineering: Familiarity with data and/or ML engineering tools and practices, including pipeline development and scalable model deployment
• Highly desirable:

Domain Knowledge – Geospatial Data: working with imagery and/or geospatial data science problems and related technical libraries such as GeoPandas

• Pragmatism : While extensive knowledge of statistical theory is highly valued, pragmatism wins over elaborate theory when it comes to shipping products that work.
• Collaboration : We believe data science is a team sport, and are after candidates who can communicate well, share knowledge, and be open to taking on ideas from anyone in the team. Having worked on shared code-bases in a commercial environment is a big plus, but it's the attitude that matters most.
• Technical Skills : A decent base of python is key to a role in the team. Other than that, we're pretty flexible - we know tools are changing rapidly, and will continue to do so for many years to come.
• Attention to detail : Showing attention to detail when it counts is important. Possesses an analytical mind and a strong nose for data issues.
• Organization: Updates tasks in Jira and keeps good notes.

Complies with responsibilities of working for a private company.

Additional Information

Some of our benefits

Nearmap takes a holistic approach to our employees’ emotional, physical and financial wellness. Some of our current benefits include:

• Quarterly wellbeing day off - Four additional days off a year as your "YOU" days
• Company-sponsored volunteering days to give back.
• Generous parental leave policies for growing families.
• Access to LinkedIn Learning for continuous growth.
• Discounted Health Insurance plans.
• Monthly technology allowance.
• Annual flu vaccinations and skin checks.
• A Nearmap subscription (naturally!).

Working at Nearmap

We move fast and work smart; often wearing multiple hats. We adapted to remote working with ease and are continually looking at ways to improve. We’re proud of our inclusive, supportive culture, and maintain a safe environment where everyone feels a sense of belonging and can be themselves.

If you can see yourself working at Nearmap and feel you have the right level of experience, we invite you to get in touch.
Watch some of our videos and find out more about what a day in the life at Nearmap looks like.

https://youtu.be/WSMYfAEdAe4

https://youtu.be/ZEGdSLWdrH0

https://youtu.be/JuHBJk2uuD8

https://youtu.be/8mSSG6uICW0

To hear an interview with Brett Tully, Director of AI Output Systems on the Super Data Science podcast, click this link: https://www.superdatascience.com/533

Mapscaping podcast: https://mapscaping.com/blogs/the-mapscaping-podcast/collecting-and-processing-aerial-imagery-at-scale

Read the product documentation for Nearmap AI:https://docs.nearmap.com/display/ND/NEARMAP+AI

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