Job Description

Verisk‘s Summer Internship Program is designed to provide you with real work experience, professional development, and networking opportunities. This is a full-time (37.5 hours/ week) paid internship program spanning ten weeks from June through August 2026. Participation in the internship program requires that you are located near the London office, as the program is a hybrid work schedule, requiring you to be in the office at least 2 days per week.

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Should you perform well in the internship, you may receive an offer to join our full-time Data Science Excellence Program (DSEP) following the Summer Internship Program (preferably July 2027).

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Responsibilities

As a data science intern you will gain hands-on experience across the data science workflow; this includes data wrangling, modelling, visualising and evaluating performance against business objectives, and productionising. You may be involved in various types of insurance including motor, home, commercial property and liability. Your work will assist in evaluating and quantifying risk, and range from extracting data from structured and unstructured sources to process improvements such as claims assessment and decision-making to help clients manage risk more effectively.

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Project span the entire product development workflow, ranging from proof-of-concept demonstrations to illustrate specific technologies, methodologies and algorithms to robust operational systems that can be put into production.   

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As a member of a project team, you will be exposed to some of the data challenges faced at Verisk’s operations potentially including large-scale data analysis (petabytes), real-time data analysis, and anomaly detection. All data sets with which you will work are of interest to risk assessment and valuation.  These data sets include insurance policy and claims data, imagery and documents.

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Qualifications

• Enrolled in the penultimate year of Bachelor's or Master’s degree with a major in Statistics, Applied Mathematics, Business Analytics, Econometrics, or related discipline.

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Experience:

• Strong programming capability in a common language such as Python and their associated ecosystem of open source libraries
• Must be proficient in SQL, understanding of NoSQL query languages a plus
• Interest in learning and using machine learning and statistical concepts (supervised, unsupervised, semi-supervised learning scenarios; optimisation and loss functions; evaluation of classification, regression, and other models; probability distributions and unbalanced samples; dimensionality problems, sampling, and Bayesian methods; missing values and imputation)

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