Lead Risk Analytics Data Scientist

Requirements

• If you enjoy working with data and analytical tools / models and have always wanted to get more involved on the business side — this role is for you
• Strong analytical and data skills spanning SQL, Python or R, pipeline development and data modeling (dbt, Snowflake), and visualization tools such as Tableau or Streamlit
• 5+ years of experience working on actuarial pricing, predictive modeling or other Risk Analytics related areas
• Ability to dive deep into data and communicate insights to audiences of all backgrounds, especially to teams with less data & analytics experience
• Relevant Actuarial experience (such as Associate of the Casualty Actuarial Society (ACAS) or equivalent exam experience) preferred but not required
• Excited and passionate about the new AI tools (Claude, Gemini, etc.) and eager to learn how that can help improve our day-to-day efficiency
• Bachelor’s degree in Actuarial Science, Mathematics, Statistics, or a related field
• Ability and interest in thinking beyond the insurance organization, considering the challenges of other teams, and prioritizing what is best for the business holistically, is essential
• Empathy, humility, and curiosity
• What the job involves
• You’ll join our centralized Risk, Analytics & Data Science team at Extend, where you’ll play an essential role in the risk monitoring process for our product protection and shipping protection offerings
• You will collaborate with cross-functional teams to help Extend maintain a healthy loss ratio through premium changes, underwriting process and claims initiatives.

A great opportunity to convert your analytics expertise into business impact by supporting measurement of unit economics to feed strong business decisions

• You will also be partnering with the cross functional teams (Product / Program, Revenue / Sales, etc.) to develop appropriate premiums for new coverages and product categories.

This isn’t a "maintain the existing model" role.

You’ll be involved from the moment a new merchant deal is being negotiated, through program design, all the way to launch

• You also have the opportunity to take ownership of data architecture for the Risk Analytics function.

We’ve invested heavily in modern data infrastructure and AI tooling, and we want someone who’s excited to actually use them.

If you enjoy autonomy and the freedom to build your own pricing infrastructure, you’ll thrive on our team

• Price deals no one has priced before.

Every time Extend enters a new merchant category, such as a new product type, a new vertical, or a partnership being negotiated right now, there’s no existing model to hand you.

You’ll design the premium structure from first principles, figure out how to validate it when the data is thin, and own it through launch.

• The work is genuinely unsolved, and the decisions are yours to make
• Turn a monitoring function into a decision engine.

We know what our loss ratios are.

What leadership needs is to know what to do about them: which claims initiatives to pursue, which premiums to adjust, which programs to renegotiate.

You’ll build the tooling that makes that call possible, and you’ll be the person making the recommendation

• Own the risk narrative with senior leadership.

When the loss ratio moves, you’re the one who explains why and what comes next, to an audience that includes the C‑suite.

If you’ve wanted more visibility than your current role gives you, this one puts you in that room

• Build the data foundation for Risk Analytics — and make it yours.

We have dbt and Snowflake.

What we don’t have is a fully built Risk Analytics layer sitting on top of it.

You’ll design the data models, set the standards, and shape how this function works for the next several years.

If you’ve ever inherited an architecture you wished you could rebuild from scratch, this is the rare opportunity to build it right the first time

• Actually use AI to change how the team works.

Not as a buzzword, but as a working practice.

We use Claude heavily.

For this role, that means finding the places in the risk analytics workflow where a manual step shouldn’t be manual, automating it, and raising the floor on what the team can produce.

• You’ll have latitude to experiment and the expectation that you will
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