Data Scientist (Insurance)

extra holidays - fully flexible
Work set-up: 
Full Remote
Contract: 
Experience: 
Expert & Leadership (>10 years)
Work from: 

Offer summary

Qualifications:

Postgraduate qualifications in data science, statistics, or actuarial studies (Masters or PhD)., Minimum of 2-3 years of practical experience in property/casualty insurance in a data science or actuarial role., Strong knowledge of insurance pricing models, rating, and regulatory requirements., Proficiency in data analysis, feature engineering, and statistical modeling using SQL and Python..

Key responsibilities:

  • Develop and refine risk models using Nearmap AI data and other sources.
  • Support insurance clients in testing, integrating, and using Nearmap data in their workflows.
  • Create and grow Nearmap's internal policy and loss database for risk insights.
  • Collaborate with insurance professionals to demonstrate the value of Nearmap's data and ensure regulatory compliance.

Nearmap logo
Nearmap Information Technology & Services SME https://www.nearmap.com/
201 - 500 Employees
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Job description

Company Description

The skys not the limit at Nearmap

We’re a SaaS company, with proprietary hardware and software that’s continuously advancing through our commitment to innovation. The sky’s the limit when it comes to what we can and plan to do for our customers. Our imagery is just the starting point. Our impact comes from our people, applying complex analysis, interpretation and artificial intelligence that opens up all sorts of possibilities for our customers.

Job Description

The Insurance AI team conducts technical work to design, develop and support products that use Nearmap and thirdparty 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 Nearmaps AIdriven 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 customerfacing solutions. The role requires someone who can seamlessly transition between technical model development work and clientfacing 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 Nearmaps own database of policy and loss data through partnership with insurers.

A portion of the Data Scientists 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 adhoc 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 customerfacing 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 (MastersPhD, or demonstrated depth of knowledge at that level), plus a minimum of 3 years practical working experience in propertycasualty 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 propertycasualty 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 – propertycasualty 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 propertycasualty 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.
        • ProgrammingTech 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 clientfacing 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 andor ML engineering tools and practices, including pipeline development and scalable model deployment

Required profile

Experience

Level of experience: Expert & Leadership (>10 years)
Industry :
Information Technology & Services
Spoken language(s):
English
Check out the description to know which languages are mandatory.

Other Skills

  • Detail Oriented
  • Collaboration
  • Communication

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