About the role

Python engineer translating complex models into high-performance code for InsurTech scale-up. Collaborate with actuarial experts and focus on optimization and numerical performance.

Responsibilities

Translate actuarial requirements (including R code prototypes) into efficient, maintainable Python
Optimize complex mathematical operations to run 10× faster using vectorization and algorithmic improvements
Implement and refactor numerical Python functions with a focus on performance and correctness
Profile and improve existing codebases that handle large analytical workloads
Build reusable numerical utilities to support actuarial analysis and diagnostics
Optimize computational bottlenecks in insurance reserving/provisioning models
Collaborate closely with actuaries and domain experts
Write clear, robust unit tests for numerical logic and edge cases

3+ years of experience using Python for numerical computing
Proven track record of writing production-quality Python code
Strong ability to translate mathematical, financial, or actuarial concepts into efficient code
Ability to write clean, modular, and testable Python
Hands-on experience improving the performance of analytical or numerical workloads
Solid understanding of vectorized computing (e.g., NumPy), numerical precision, stability, and performance trade-offs
Experience with packaging, virtual environments, and dependency management
Comfortable with agile workflows and project-tracking tools such as Jira
High familiarity with git, GitHub, branching strategies, and resolving merge conflicts
Gives and receives code reviews constructively and provides feedback on pull requests
Works independently from stories and specifications written by non-engineer domain experts
High level of written and spoken English