Data Science | 2026 Summer Internship Program

Verisk Analytics-posted 3 months ago
Full-time • Intern
Jersey City, NJ
Professional, Scientific, and Technical Services
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Verisk‘s Summer Internship Program is designed to provide you with real work experience, professional development, and networking opportunities. This is a full-time (40 hours/ week) paid internship program spanning ten weeks from June through August 2026. Participation in the internship program requires that you are located near your assigned office as the program is a hybrid work schedule, requiring you to be in the office at least 2 days per week. We are hiring in the following U.S. location: Jersey City, NJ. We have opportunities for students interested in exploring careers in tech around the globe. Verisk is a leading data analytics provider serving customers in insurance. Our team of nearly 7,000 helps customers make crucial decisions every day about risk—with greater precision, efficiency, and discipline. Should you perform well in the internship, you may receive an offer to join our full-time Data Science Excellence Program (DSEP) following graduation. As DSEP is a rotational program, we highly suggest that you apply to this internship if you are open to future relocation.

  • Receive hands-on analytics experience doing data wrangling, querying, modeling, visualizing, and productizing.
  • Work in business areas including insurance, finance, energy, and climate.
  • Help clients in decision analytics, forecasting, and risk management.
  • Create proof-of-concept demonstrations to illustrate specific technologies and algorithms.
  • Develop robust operational systems that can be put into production.
  • Participate in project teams to address data challenges faced at Verisk's operations.
  • Graduate pursuing a BS or MS degree in a technical field of study (data science, statistics, business analytics, engineering, etc.).
  • Strong programming capability in a common language such as Python or R and their associated ecosystem of open-source libraries.
  • Proficient in SQL; understanding of NoSQL query languages is a plus.
  • Interest in learning/using machine learning and statistical concepts.
  • Experience with supervised, unsupervised, and semi-supervised learning scenarios.
  • Familiarity with optimization and loss functions.
  • Knowledge of evaluation of classification, regression, and other model results.
  • Understanding of probability distributions and unbalanced samples.
  • Experience with dimensionality problems, sampling, and Bayesian methods.
  • Ability to handle missing values and imputation.
  • Paid internship program.
  • Mentorship and training.
  • Engaging program events.
  • Exposure to senior analytics and business leaders.
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