Staff Data Scientist, Insurance

Coalition, Inc.

4h•Remote

About The Position

We are hiring a Staff Data Scientist to define and lead the data science strategy for Coalition’s insurance business. In this role, you will own some of our most important analytical and modeling problems across underwriting, claims, CoalitionRe, and portfolio management helping us to move the organization from ad hoc analyses to reusable, rigorous decision frameworks. You’ll lead multi-quarter, cross-functional initiatives, design and analyze complex experiments and quasi-experiments, and build data and modeling assets that become the source of truth for leaders. Your work will shape how we measure performance, price and select risk, structure reinsurance, and allocate capital, while mentoring other scientists and analysts to raise the technical bar across the team.

Requirements

10+ years in analytics or data science with a consistent track record of measurable product, underwriting, or revenue impact.
Deep domain experience (8+ years) in the Insurance industry, ideally P&C or cyber/E&O, working closely with underwriting, claims, actuarial/portfolio, or reinsurance teams.
Expert SQL, including complex data modeling, performance optimization, and clear, maintainable documentation.
Expert in Python or R for analysis, experimentation, and reproducible workflows (e.g., notebooks, scripts, modular libraries).
Strong grasp of statistical methods: hypothesis testing, experiment design, causal thinking (e.g., quasi-experimental designs, difference-in-differences, propensity scores), and robust interpretation under real-world constraints.
Experience with BI tools such as Looker or Tableau, and the ability to design reliable, executive-ready dashboards and self-serve measurement layers.
Excellent communication and stakeholder management skills; able to translate complex technical work into clear, actionable language for senior non-technical audiences.
Demonstrated ability to lead cross-functional initiatives end-to-end: framing ambiguous problems, aligning stakeholders, delivering high-quality work, and landing the change in partner teams.

Nice To Haves

Exposure and usage of AI-assisted coding IDEs like Cursor or GitHub Copilot to improve development and analysis velocity.
Technical rigor to move the team from “one-off” reports to repeatable AI frameworks.
Domain experience in fintech or adjacent regulated industries in addition to insurance.
Familiarity with broader go-to-market and servicing tools and data, such as Looker, Amplitude, Google Analytics, Marketo, Salesforce, Segment, or Zuora.
Experience with analytics and data workflows such as dbt or Airflow, especially for productionizing analytical datasets or model pipelines.
Hands-on experience with causal inference methods and quasi-experimental designs, including applying them in noisy, operational insurance environments.

Responsibilities

Define and lead the data science strategy for Coalition’s insurance business, driving multi-quarter initiatives across underwriting, claims, CoalitionRe, and portfolio management.
Lead cross-functional analytics and data science projects that uncover insights and optimization opportunities across the end-to-end customer and broker journey, from submission through renewal and expansion.
Provide technical leadership and guidance to other data scientists and senior analysts, setting a rigorous technical bar and developing repeatable frameworks (e.g., experimentation, causal analysis, model evaluation) that move the team away from one-off analyses.
Design and analyze experiments and quasi-experiments (e.g., guideline changes, pricing strategies, broker programs), establishing best practices for test design, power, guardrails, and interpretation in low-frequency / high-severity insurance settings.
Define, document, and maintain core insurance funnel and portfolio metrics (e.g., submission-to-bind, hit rate, loss ratio, frequency/severity, attachment points), and build automated dashboards and reporting used by leadership for decision-making.
Develop data models and scalable analytical / ML workflows that improve reliability, repeatability, and time-to-insight for insurance stakeholders and downstream teams.
Analyze disparate data sources (policy, exposure, pricing, claims, broker behavior, reinsurance, external data) and synthesize findings into clear, influential narratives with prioritized recommendations.
Influence product, insurance, and revenue leadership with data-backed recommendations, shaping roadmaps, investment decisions, and OKR tracking.
Mentor and uplevel junior and senior analysts/scientists, contributing to team best practices, code quality, documentation, and review processes; help shape the data science culture and career pathways.
Champion innovation in methods and tooling, including causal inference, advanced statistical techniques, and appropriate application of ML/AI to unlock new capabilities and scale impact.