Data Scientist (Insurance)
Data Scientist (Insurance)
Employer Active
Job Alert
You will be updated with latest job alerts via email
Share
Job Alert
You will be updated with latest job alerts via email
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 Insurance Data Scientist plays a key role in helping insurance companies unlock the value of Nearmaps AI-driven data within their pricing underwriting modelling and regulatory workflows. This includes contributing actuarial expertise to ensure that risk and damage classification scores predictive models and derived insights are statistically robust relevant to insurance use cases and suitable for integration into rating plans filings and customer-facing solutions. 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 significant portion of the Insurance 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 ad-hoc analyses. As such experience working as a data scientist or actuary in the property casualty (P&C) insurance space is critical.
The Insurance 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.
This role is ideal for a data scientist with a strong actuarial background who is excited to work at the intersection of AI geospatial data and insurance and who is passionate about helping insurers improve their pricing accuracy operational efficiency and regulatory outcomes using innovative data sources.
Skills & Experience we are looking for:
- 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.
- 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 its the attitude that matters most.
- Technical Skills: A decent base of python is key to a role in the team. Other than that were 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.
Highly desirable:
- Domain Knowledge Geospatial Data: working with imagery and/or geospatial data science problems and related technical libraries such as GeoPandas
- Data / ML Engineering: Familiarity with data and/or ML engineering tools and practices including pipeline development and scalable model deployment
Qualifications :
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.
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. Were 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.
hear an interview with Brett Tully Director of AI Output Systems on the Super Data Science podcast click this link: podcast: the product documentation for Nearmap AI: but we got this! Nearmap does not accept unsolicited resumes from recruitment agencies and search firms. Please do not email or send unsolicited resumes to any Nearmap employee location or address. Nearmap is not responsible for any fees related to unsolicited resumes.
Remote Work :
No
Employment Type :
Full-time
Employment Type
Full-time
