How to Hire AI, ML and Data Engineers for Insurance: Answers for Carriers, MGAs and Insurtechs

October 25, 2026 · Chris Stinson, Managing Partner, The Kas Group

Short answer: Most insurers should start their first AI or data hire with the data platform, because models are only as good as the policy, billing and claims data under them. Whatever the role, screen for production readiness: people who have shipped and run models or pipelines on real insurance data.

This guide is for CIOs, heads of data, analytics leaders, chief underwriting officers and insurtech CTOs at carriers, MGAs and insurtechs. For how we run an insurance search with you, and the kinds of insurance work we hire for, see our insurance recruiting page.

Which Insurance Data and AI Roles Are Being Hired Now

Live insurance postings in October 2026 show clear patterns:

Data platform first. Many carriers are hiring data engineers and data architects to modernize pipelines for reporting, analytics and actuarial use before any data science seat exists. Snowflake, lakehouse and data product models come up often.
The first seat. Several postings describe the first dedicated AI engineer, or ask one person to stand up the foundational platforms and build models too. Others ask a new leader to build a data function from scratch.
Hybrid roles. One person often spans data engineering, BI, automation and AI. Health plans add healthcare quality data such as HEDIS.
Common titles. Data engineer and senior data engineer, AI engineer, data architect, data scientist and predictive modeler, analytics engineer, BI leads, and data leadership roles from director to VP of data and AI.
Skills named. Pricing analytics, claims AI, underwriting and decision intelligence, LLM and RAG builds, MLOps, data governance and metric definitions, process automation, and reinsurance and reserving analytics.

Role by Role: What Each Insurance Data and AI Hire Does

Data engineer

Builds the pipelines that join policy, billing, claims and third party data, often on Snowflake or a lakehouse. Pulls data out of legacy core systems and delivers data quality the actuarial team can trust. Seats range from intermediate to lead or manager.

ML engineer or AI engineer

Takes models into production and keeps them running: deployment, MLOps, monitoring and LLM or RAG applications. When this is the first AI seat, the person is expected to set the patterns, playbooks and guardrails everyone after them follows.

Actuarial data scientist or predictive modeler

Works on pricing and reserving analytics, using GLMs plus machine learning and the pricing tools your team already runs. Must explain the work to actuaries and, through rate filings, to regulators.

Analytics engineer

Builds governed data models, metric definitions and BI standards, often in Power BI or a semantic layer. Bridges data engineering and business reporting so every team reads the same numbers.

Claims and underwriting AI

Claims AI and analytics engineers, decision intelligence engineers and document AI specialists. Success is measured by adoption: adjusters and underwriters using the tools in their daily work.

Data architect or data leader

Owns the roadmap and the modernization plan. Leader seats often come before the team is built.

For the general version of these roles, read how to hire AI and ML engineers and data engineer vs analytics engineer.

Your First Hire: Leader, Builder or Hybrid

If no one owns data strategy and governance, a leader first can set the roadmap and make the next hires count. If a leader is in place, hire the builders. If policy and claims data are not yet reliable, the builder is a data engineer, not a data scientist.

A hybrid first seat can work well. It also narrows the pool, so decide which parts of the job matter most before the search starts. For founding data teams in general, see how to hire senior data engineers.

What to Look For When Screening Insurance AI and Data Candidates

Shipped work in production, with evidence of monitoring and retraining.
Experience with messy, legacy or regulated data.
The ability to explain a model to an actuary, an underwriter or a regulator, and documentation for model governance and bias testing.
Fit for hybrid scope when the role is a first seat.
A stack match on the true non negotiables only.

At The Kas Group, Derek Norton, PhD in Statistics, vets every AI, ML and data candidate for production readiness. Read more in our vetting methodology and our senior data engineer skills assessment. Reference checks round it out.

What Shapes the Hiring Timeline

Every search is different. These factors move it most:

Seniority of the seat.
How broad the hybrid scope is.
On site, hybrid or remote.
How your pay lines up with the market.
Whether insurance background is required.
How fast your interview loop runs.
Whether decision makers agree on the role up front.

Frequently Asked Questions

What should an insurer's first AI or data hire be?

For most carriers and MGAs, the first hire is a data engineer or data platform lead, because policy, billing and claims data must be reliable before models can help. Hire an AI or ML engineer first only when that data is already usable. The right answer depends on the outcome you need in the first 90 days.

Do we need a data scientist or an ML engineer?

A data scientist frames the business problem and builds the model, such as a loss ratio or retention model. An ML engineer puts that model into production and keeps it running inside your policy and claims systems. Smaller carriers often need one hybrid person who can do both, which narrows the candidate pool.

How is hiring a data engineer for insurance different from other industries?

Insurance data engineers work with legacy policy admin and claims systems, ACORD formats, third party data and actuaries who depend on every number. Audit and regulatory scrutiny never stops. Screen for people who have pulled clean data out of messy core systems, not only people who know modern tools like Snowflake.

What does an actuarial data scientist do, and how is it different from an actuary?

An actuarial data scientist works beside actuaries on pricing and reserving, using machine learning alongside the GLMs actuaries already trust. They must explain their methods in terms actuaries accept and that hold up in a rate filing. They are not a credentialed actuary unless your role specifically requires the credential.

What is an analytics engineer, and does an MGA need one?

An analytics engineer builds governed, tested data models and clear metric definitions between data engineering and BI. Many MGAs benefit early, because carrier reporting, bordereaux and partner data all need consistent numbers. If your premium or loss figures change depending on which report you open, this hire is usually the fix.

What skills matter for claims automation and claims AI roles?

Look for experience with claims intake and routing, severity prediction, document AI on claim files, and fraud signals that keep false positives manageable. Just as important, the person must work well with adjusters and SIU, because a claims model only pays off when the people handling claims actually use it.

What should we look for in an underwriting ML hire?

Look for risk scoring, submission triage and appetite matching experience, with outputs underwriters can understand and trust. The best candidates fit models into the existing underwriting workflow instead of trying to replace underwriter judgment. Ask how they explained a score to an underwriter and what they did when the model got a referral wrong.

How do we tell if a candidate can ship models to production?

Ask for models they monitored after launch, how they caught and handled drift, and what documentation they wrote for model risk review. At The Kas Group, every AI, ML and data candidate is vetted by Derek Norton, who holds a PhD in Statistics, for production readiness before you meet them.

Does the candidate need insurance experience?

Insurance experience helps, but it is not always required. Experience in another regulated industry, such as banking or healthcare, often transfers well to policy and claims data. Decide before the search starts whether an insurance background is a non negotiable or a nice to have, because that choice changes the size of the pool.

How do LLMs and agentic AI fit into insurance hiring now?

More insurance postings now ask for LLM, RAG and agentic builds on top of the existing data warehouse, often for claims notes, submissions and policy documents. Governance comes with them. Screen for how a candidate sets guardrails and evaluates output quality in a regulated setting, not for how impressive a demo looks.

How long does it take to hire an ML or data engineer for an insurer?

It depends on seniority, remote or on site policy, how your pay compares with the market, how broad the hybrid scope is, and how fast your interview loop moves. Narrow hybrid roles and on site requirements take longer. Agreeing on pay and scope before the search starts is the surest way to shorten it.

Should we hire a data leader before the team?

If no one owns data strategy and governance today, hire the leader first so the roadmap, standards and first hires follow one plan. If a data leader is already in place, hire the builders that leader needs. Many carriers are making exactly this choice now as they modernize their core data platforms.

How The Kas Group Helps Insurers Hire AI, ML and Data Engineers

The Kas Group helps insurance carriers, MGAs and insurtechs hire AI engineers, machine learning engineers, data engineers and analytics engineers. Chris Stinson, Managing Partner, brings 12 years in recruiting and works with your team to define the hire before the search starts. Every AI, ML and data candidate is personally vetted by Derek Norton, PhD in Statistics, so you meet people ready for production insurance work. Searches are quoted per role. See how AI and data recruiting and our programs work.

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