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This role is not eligible for employer-sponsored work authorization (e.g., H-1B, OPT, CPT, etc.) or transfer. Candidates must have valid U.S. work authorization. PLEASE NO THIRD PARTIES.
Position Overview We are seeking a Data Validation Engineer to support Quality Assurance across our cloud-based SaaS products. This role focuses on validating scientific and financial algorithms, building automated test suites, and ensuring the accuracy and reliability of our catastrophe-modeling platform.
About CRS CRS (Catastrophic Risk Solutions) builds advanced stochastic models that simulate catastrophic events—hurricanes, earthquakes, floods, and more—to help the global insurance industry make objective, data-driven decisions. Our Monte Carlo simulation engines generate hundreds of thousands of years of synthetic event data, enabling insurers and communities worldwide to better understand and manage risk.
We hire smart, curious people who understand both the science and the mission. If this resonates with you, we’d love to talk.
Responsibilities
Ensure products meet strict accuracy requirements for scientific, probabilistic, and financial algorithms.
Partner with developers to identify test coverage gaps and define testing strategies across the full product.
Develop unit and integration tests in C# and Python, optimized for AWS execution with minimal compute footprint.
Build and maintain API-driven automated tests to validate core system functionality and determine pass/fail outcomes.
Document test cases clearly and integrate all testing into our automated QA framework.
Participate in Agile Scrum teams and manage concurrent day-to-day tasks.
Apply quantitative and analytical reasoning to validate data, models, and simulation outputs.
Identify and troubleshoot complex issues using structured, methodical problem-solving approaches.
Qualifications Required:
Bachelor’s or Master’s degree in a STEM field (data science, engineering, mathematics, finance, economics, etc.).
2–4 years of QA experience within an Agile software development environment.
Experience testing or developing cloud-native, serverless, or SaaS products.
Strong analytical programming skills with Python or R, including libraries such as Pandas, Tidyverse, or DataFrames.
Proficiency with relational databases and SQL; familiarity with advanced on-prem or cloud database systems.
Experience with object-oriented languages such as C++, C#, or Java.
Experience with API testing tools (e.g., Postman) and E2E frameworks (e.g., Cypress).
Excellent communication skills and ability to collaborate with cross-functional teams.
Strong aptitude for quantitative problem-solving and advanced analytics.
Preferred
Experience designing or validating numerical probabilistic models in engineering, catastrophe modeling, finance, or actuarial science.
Familiarity with statistical modeling tools such as R or MATLAB.
Background in scientific computing, simulation modeling, or quantitative risk analysis.
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Sprachkenntnisse
- English
Hinweis für Nutzer
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