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Data Analyst - London

Ref: 42 Date Posted: Thursday 09 Aug 2018
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At FloodFlash, we are on a mission to create a new way of thinking about insurance. Recently backed by two leading VCs, we are looking to grow our analytical team. This is a unique opportunity to shape
the technical development behind our pioneering insurtech product.

Why Join Us?
• People: we are insurtech experts, including consultants, engineers, PhDs & published research scientists
• Position: we just closed a seed funding round with two of Europe’s leading venture capital funds
• Product: we have insurance capacity, independent FCA authorisation & our first live policies in place
• Package: we hire and pay for the best including a significant equity stake
• Potential: we are building a company to solve the $140bn in uncovered annual catastrophe losses


We are looking for a Data Analyst to support development of our risk pricing methodology. You will work with various sources of data (open and proprietary) to enhance our view of flood risk, enabling more accurate pricing and wider availability of affordable flood insurance.
Analytics are at the heart of the company, playing a key role in our approach to risk pricing, claims, strategic growth and development into new markets. Our vision brings together diverse fields such as Internet of Things, machine learning, catastrophe modelling, and smart contracts, to support a seamless insurance product.
As an early employee, you will have the opportunity to grow with the role and shape the development of our technical capabilities. Working in a small team, you will also be expected to be able to work independently to deliver clear and justified insights and solutions to complex problems.

Ideally, you will have experience or knowledge relating to some, although not necessarily all, of the following. That said, aptitude and passion for working with data are more important than specific experience:
• Track record using data-driven approaches to solve real-world problems
• Working with data from multiple sources
• Statistical modelling, probability and distributions
• Scientific programming in languages such Python, R or Matlab
• Databases and SQL, especially postgres, including writing queries and managing schemas
• Geospatial data analysis and GIS tools (e.g. QGIS, PostGIS, ArcMap)
• Hydrology and/or flood risk modelling
• Catastrophe risk and insurance pricing