Latest Posts:

Sorry, no posts matched your criteria.

Follow Us:

Back To Top

JOIN OUR EFFORTS TO

reinvent catastrophe modelling

THROUGH VENTURE.

JOIN OUR EFFORTS TO

reinvent catastrophe modelling

THROUGH VENTURE.

Our economy does not price environmental cost into the goods and services it produces. Instead, businesses are incentivised to produce as cheaply as possible, with pollution and environmental degradation being external costs borne by society.

Insurance is a mechanism for reducing social burden. When economic losses arise from pollution and other climate events, insurance spreads the costs in an equitable manner and reduces the losses suffered by society, with significant portions of the costs being redistributed to financial institutions.

Unfortunately, an inability to predict the full cost of climate events has led to a climate insurance crisis. Economic losses are growing and U.S. insurers have suffered losses 82% above average since 2000. This has lead to an increasing climate insurance protection gap.

 

Our approach

Catastrophe modelling relates natural disasters to future payouts. With the advent of climate change, however, nature is no longer behaving like it used to, and past data is becoming less predictive of future events. We also have less historic data than we’ve ever had – emerging environmental phenomenon such as wildfires are thus named because they haven’t happened at this scale of loss before. Many are trying to solve this problem by harvesting ever more data on the physical characteristics of buildings through satellite imagery and LIDAR technology. We believe this methodology is fundamentally flawed: the best indicators of housing integrity are not visible externally (and thus not obtainable through satellite imagery, no matter the resolution quality) whilst deteriorating building quality is not what is driving increasing insured losses – changes in urbanisation and socioeconomic factors are. We are looking to revolutionalise catastrophe modelling by using machine learning, big data and cutting-edge demographic research to build new methods for loss estimation based on a deep understanding of the urban environment rather than purely physical building characteristics.

In order to achieve this, we’re looking to develop:

• efficient and robust ETL processes

• advanced feature extraction capabilities (machine learning, computer vision, OCR)

• APIs for connection to data sources such as Google Street View, NASA satellite imagery, infrastructure documents, city planning documents etc.

• back-end compatibility with exiting open source catastrophe modelling frameworks such as Oasis

• a front-end for interaction with our loss estimation modules

• innovative vulnerability curve methodologies

 

The Opportunity for you

Joining DSV is a fantastic opportunity to develop concepts for and launch your own startup. Working with us, our sector-specific advisors and our current founders, you’ll have access to a completely new kind of environment for creating science-driven companies, one that does not start with university IP, but with a systematic rework of an opportunity area from first principles.

Given the current stage of the project, we expect less than 3 months until incorporation and spin-out to start the early tech development / proof-of-concept work with investment secured from DSV, and other funding sources.

 

Our offer

We’ll provide a competitive monthly salary while you transition out of your current role and into starting the new venture, and grant you 80% equity ownership split between you and your co-founder (compared to <49% TTO standards).

 

Our impact

In the last couple of years we’ve built and invested in 5 brand new companies in the energy sector, both software and hardware, across high performance materials, energy storage, hydrogen and carbon capture.

 

Who should apply

We’re after two different sets of expertise so apply if you have background in:

EITHER

• end-to-end software development experience (C/C++; Java; Python; SQL; JavaScript; React)
• significant back-end data engineering experience, having ideally developed APIs and ETLs in the past
• experience working with big data (Apache Spark, PySpark, Kubernetes, Docker, AWS, Azure, DynamoDB)
• experience building ML/AI/Computer vision models and undertaking advanced feature extraction from GIS and/or text documents

ideally, combined with knowledge in any of the following:

• experience with PostGIS familiarity with open data formats i.e. OSM
• knowledge of ETL scheduling
• knowledge about catastrophe modelling and vulnerability curves
• experience in insurance / insurance risk modelling

OR

•  knowledge of catastrophe modelling, vulnerability curves and exposure databases
•  strong insurance network ideally with experience collaborating closely with regulators
•  deep understanding of insurance ecosystem (insurance, insurance broking, reinsurance)

ideally, combined with knowledge in any of the following:

•  experience with multiple model vendors (AIR, RMS, CoreLogic etc.)
•  familiarity with OASIS and other open-source methodologies
•  interest in the ILS and disaster relief spaces

OUR OFFER

Taking a Co-Founder role at DSV is a fantastic opportunity to develop concepts for, and launch your own startup. Working with us, we’ll pay you to start your own company with access to co-founders and pre-seed funding. We’ll also continuously support you in building the venture and developing early data to land your first customers and investors.

Compared to other Entrepreneur in Residence programmes, DSV’s is friendly to individuals with no previous founder experience and comes with a commitment from us to support your company throughout its journey.

Our economy does not price environmental cost into the goods and services it produces. Instead, businesses are incentivised to produce as cheaply as possible, with pollution and environmental degradation being external costs borne by society.

Insurance is a mechanism for reducing social burden. When economic losses arise from pollution and other climate events, insurance spreads the costs in an equitable manner and reduces the losses suffered by society, with significant portions of the costs being redistributed to financial institutions.

Unfortunately, an inability to predict the full cost of climate events has led to a climate insurance crisis. Economic losses are growing and U.S. insurers have suffered losses 82% above average since 2000. This has lead to an increasing climate insurance protection gap.

 

Our approach

Catastrophe modelling relates natural disasters to future payouts. With the advent of climate change, however, nature is no longer behaving like it used to, and past data is becoming less predictive of future events. We also have less historic data than we’ve ever had – emerging environmental phenomenon such as wildfires are thus named because they haven’t happened at this scale of loss before. Many are trying to solve this problem by harvesting ever more data on the physical characteristics of buildings through satellite imagery and LIDAR technology. We believe this methodology is fundamentally flawed: the best indicators of housing integrity are not visible externally (and thus not obtainable through satellite imagery, no matter the resolution quality) whilst deteriorating building quality is not what is driving increasing insured losses – changes in urbanisation and socioeconomic factors are. We are looking to revolutionalise catastrophe modelling by using machine learning, big data and cutting-edge demographic research to build new methods for loss estimation based on a deep understanding of the urban environment rather than purely physical building characteristics.

In order to achieve this, we’re looking to develop:

• efficient and robust ETL processes

• advanced feature extraction capabilities (machine learning, computer vision, OCR)

• APIs for connection to data sources such as Google Street View, NASA satellite imagery, infrastructure documents, city planning documents etc.

• back-end compatibility with exiting open source catastrophe modelling frameworks such as Oasis

• a front-end for interaction with our loss estimation modules

• innovative vulnerability curve methodologies

 

The Opportunity for you

Joining DSV is a fantastic opportunity to develop concepts for and launch your own startup. Working with us, our sector-specific advisors and our current founders, you’ll have access to a completely new kind of environment for creating science-driven companies, one that does not start with university IP, but with a systematic rework of an opportunity area from first principles.

Given the current stage of the project, we expect less than 3 months until incorporation and spin-out to start the early tech development / proof-of-concept work with investment secured from DSV, and other funding sources.

 

Our offer

We’ll provide a competitive monthly salary while you transition out of your current role and into starting the new venture, and grant you 80% equity ownership split between you and your co-founder (compared to <49% TTO standards).

 

Our impact

In the last couple of years we’ve built and invested in 5 brand new companies in the energy sector, both software and hardware, across high performance materials, energy storage, hydrogen and carbon capture.

 

Who should apply

We’re after two different sets of expertise so apply if you have background in:

EITHER

• end-to-end software development experience (C/C++; Java; Python; SQL; JavaScript; React)
• significant back-end data engineering experience, having ideally developed APIs and ETLs in the past
• experience working with big data (Apache Spark, PySpark, Kubernetes, Docker, AWS, Azure, DynamoDB)
• experience building ML/AI/Computer vision models and undertaking advanced feature extraction from GIS and/or text documents

ideally, combined with knowledge in any of the following:

• experience with PostGIS familiarity with open data formats i.e. OSM
• knowledge of ETL scheduling
• knowledge about catastrophe modelling and vulnerability curves
• experience in insurance / insurance risk modelling

OR

•  knowledge of catastrophe modelling, vulnerability curves and exposure databases
•  strong insurance network ideally with experience collaborating closely with regulators
•  deep understanding of insurance ecosystem (insurance, insurance broking, reinsurance)

ideally, combined with knowledge in any of the following:

•  experience with multiple model vendors (AIR, RMS, CoreLogic etc.)
•  familiarity with OASIS and other open-source methodologies
•  interest in the ILS and disaster relief spaces

our offer

Taking a Founder role at DSV is a fantastic opportunity to develop concepts for, and launch your own startup. Working with us, we’ll pay you to start your own company with access to co-founders and up to £500k in pre-seed and seed funding. We’ll also continuously support you in building the venture and developing early data to land your first customers and investors.

Compared to other Entrepreneur in Residence programmes, DSV’s is friendly to individuals with no previous founder experience and comes with a commitment from us to support your company throughout its journey.

Interviews ongoing

Interviews ongoing

Join the team

current Founders

Lydia is a statistician interested in using data to improve market performance for environmental outcomes. She is a Palantir alumnus with a degree in statistics from Berkeley, who previously worked in bioinformatics at Cancer Research UK.

Join the team

current Founders

Lydia is a statistician interested in using data to improve market performance for environmental outcomes. She is a Palantir alumnus with a degree in statistics from Berkeley, who previously worked in bioinformatics at Cancer Research UK.