Junior Model Developer

at  hyperexponential

MODEL DEVELOPMENT AT HYPEREXPONENTIAL

The hyperexponential Model Development team (MDT) is a unique blend of engineers, actuaries, mathematicians and data scientists. We don’t take on traditional “transformations” (we have brilliant partners for that) – we specialise in handling only the most difficult and interesting problems that our customers face, where only a combination of technical depth, market expertise and hx Renew knowledge will do.

REQUIRED SKILLS & EXPERIENCE

• A flair for Python - we’ll take you to the next level here but you’ll need production grade Python experience to hit the ground running
• Any experience using predictive modelling and advanced analytics methods (our preference would be for any exposure to insurance or reinsurance pricing techniques, but we’re open to different backgrounds)
• A self-starter attitude - you can operate autonomously within a high-trust, high-accountability environment. You proactively drive your own personal development and stay up-to-date with the latest industry/tech trends and challenges
• Evidence of a passion for experimenting with new approaches and technologies both through your work and extracurricular learning - we host a lot of hackathons so you won’t be short of opportunities!

• Learn the hx Renew modelling platform - you’ll ship your first models independently within days!
• Build models in Python for seamless integration, using cutting edge approaches
• Work directly with customer actuarial teams, partner developers and our internal development teams to solve the most complex coding problems using Python
• Develop cutting-edge technical solutions while working with incredibly bright, passionate, ambitious people
• Review our existing Python model development approach and contribute to our best-practice guides
• Consistently contribute to and improving our internal and external training & development materials
• Day-to-day quality assurance process of in-flight client and internal projects
• Always seek out opportunities to improve our model development standards and ways of working