Actuarial Intern — Data & Tools Focus | Hybrid, Paid

Aviva plc; Warszawa; Hybrid; PLN 22,000 - 33,000; Full time

Benefits offered by this job

Hybrid working model

Paid internship

International organization

Job summary

Aviva plc's Services Excellence Centre in Warsaw offers a 6-month paid internship for students or graduates in quantitative fields. The role follows a hybrid model, with 3–4 days per week on-site in the Warsaw office and remote work as needed, in an international organization.

You will collaborate with actuarial, business and development teams, prepare and validate data for models, and contribute to tool development while learning about data analysis and process improvements in a dynamic

Qualifications

• Student or graduate in a quantitative field such as mathematics, economics, statistics, or computer science.
• Intermediate level of English.
• Excel proficiency (VBA is a plus).
• Basic programming skills (Python preferred).
• Availability for 3-4 days per week.
• Strong analytical skills.
• Creativity and enthusiasm for work.
• Strong communication skills.

Responsibilities

• Collaborating with actuarial, business and development teams to support project delivery.
• Preparing and validating data used in actuarial and forecasting models.
• Participating in the development of actuarial tools.
• Conducting quantitative and qualitative analysis of the insurance and asset portfolios.
• Improving and automating processes.
• Supporting the team with test execution and documenting test results.

Skills

Analytical skills

Creativity

Communication skills

Excel proficiency

Python basics

Education

Quantitative field student or graduate

Tools

Excel

Python

VBA

Job description

Aviva plc's Services Excellence Centre in Warsaw offers a 6-month paid internship for students or graduates in quantitative fields. The role follows a hybrid model, with 3–4 days per week on-site in the Warsaw office and remote work as needed, in an international organization.

You will collaborate with actuarial, business and development teams, prepare and validate data for models, and contribute to tool development while learning about data analysis and process improvements in a dynamic