Senior Model Validation Analyst II(Python/R)

We then run Monte Carlo simulations to provide hundreds of thousands of years of simulated events. These help the insurance industry make objective and data driven decisions based on their risk tolerances. As a company, we have a strong sense of purpose and know we are helping resilient communities worldwide.

• This role is focused on Extreme Event Risk Models, doing Quality Assurance on the models mentioned in the description and developed at Verisk.
• You will partner with scientists and structural engineers and identify areas for model validation, with an emphasis on developing creative approaches to expand test coverage.
• Additionally, you will create detailed test plans and strategies to ensure the model has full QA coverage.
• You will ensure the products meet exacting requirements for accuracy and explicit and/or implicit validation of scientific, engineering, and financial algorithms.
• You will, under minimal supervision, be constantly manipulating and transforming data, performing advanced statistical and spatial analysis to validate complex model components, and summarizing findings to be presented at all levels of Verisk.
• You will author technical documents in Python’s Jupyter Notebook or in R Markdown detailing your validation work.
• You will participate in cross-functional Agile Scrum teams while delivering concurrent day-to-day tasks and use automated testing practices throughout the software development life cycle.
• Successful candidates will use their strong quantitative data analytics mindset to deliver strategic projects using robust methodologies.
• Position requires a deep commitment to quality assurance that leverages the best practices already in place and help to enhance them.

• Candidates must have a graduate degree in STEM related areas (data science, engineering, science, mathematics, statistics, finance, economics), and a minimum of 4-6 years of relevant work experience.
• Must have proven experience in analytical programming, fluency in languages like Python or R, DB experience such as SQL, and knowledge of libraries like Pandas, Tidyverse, Data frames.
• Candidates must have strong experience working with GIS tools and large data sets, performing analysis, manipulations, and data visualizations.
• Experience with designing and/or validating numerical probabilistic models in engineering, science, catastrophe modeling, finance, actuarial science, etc.
• Candidates must have excellent attention to detail and experience deriving actionable insights from data.