Description:

Our client is a global leader in climate catastrophe risk modeling, providing advanced solutions to insurers, governments, and financial institutions.
They offer models covering over 190 countries, delivering high-resolution risk assessments for inland flooding, storm surges, tropical cyclone wind, severe convective storms, and wildfires.
The company utilizes state-of-the-art probabilistic modeling, which is known for its speed, technical excellence, and transparency.
As a pioneer in integrating climate change scenarios, their product helps clients enhance resilience and financial sustainability in an increasingly volatile environment.
Their proprietary financial model enables smarter underwriting, investment, and mitigation strategies.
The company is committed to transparency, innovation, and customer-centricity, aiming to shape a resilient future for generations to come.

Requirements:

Candidates must have 5+ years of experience in data science, statistical modeling, or machine learning.
Proficiency in Python and/or R is required, along with experience in data manipulation, visualization, and ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.
A strong understanding of probabilistic modeling, statistical inference, and Bayesian methods (if applicable) is necessary.
Hands-on experience with large-scale datasets and distributed computing frameworks like Spark or Dask is essential.
Excellent communication skills and the ability to work in a collaborative, fast-paced environment are required.