Soteris (YC S19) – hiring employees #3-5 in SF to lower insurance pricing

Hi – my name is Sunit. I founded Soteris because the way insurance rates are currently set results in massive inefficiencies that increase prices for policyholders like you and me. From years of experience, I know for a fact that a machine learning approach is orders of magnitude better than what the largest insurers do – and our customer list is proof of that claim.

Before Soteris, I built a $750 million property and casualty (everything that’s not life or health) insurance company from scratch, out of a $16 billion hedge fund called Pine River Capital. This gave me deep visibility into how insurance works, through which I saw firsthand how much of the insurance value-chain is terribly outdated and littered with processes that might have made sense in 1920 – but are way past their prime in 2020.

These inefficiencies lead to increased prices for policyholders, and they can all be solved by better use of existing data, so I started Soteris to do just that. In a short time, Soteris showed its first customer that our software could solve these problems to the tune of more than doubling their policy profitability. They plan to drop rates for at least 80% of their applicants as a direct result of the efficiencies Soteris’s software provides. I think that’s pretty cool.

Soon after, Soteris signed two enterprise production contracts providing almost $1m in contract revenue – at which point it was still just me at the firm, so I started to build out the team from there.

Today that team is two PhDs with over 20 combined years of experience deploying algorithms in financial markets. We just raised a large round from a number of top investors, including YC, Amplify Partners, Khosla Ventures, and Data Collective. Combine that with a lot of early revenue and a low burn rate, and we have a long, ample runway to execute our mission. What else would you expect from a team from finance?


We’re primarily looking to build the team along two verticals: machine learning and back-end infrastructure. We work in Python, but the specific toolset you’ve used is less critical than your ability to adapt. You’d be doing the following:

* Defining our technical strategy and direction during a critical period in our growth

* Helping build and maintain our cloud infrastructure

* (ML specifically) Building the algorithms we use to produce the output and analysis that we feed back into customer workflows, including data exploration, model selection, hyperparameter tuning, and iterative analysis

* Creating data processing pipelines to simplify the onboarding and normalizing of highly heterogeneous customer data sets

* Establishing the coding standards and agile and collaborative engineering culture and that will allow the whole team to thrive

* Setting up our processes to optimize the accuracy/speed-of-delivery tradeoff as you see fit (e.g. unit tests, automation, our code review process, etc.)

* Working with clients to understand requirements, formulate use-cases, and build pragmatic solutions

If you’re interested, please apply here:

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