Senior Data Scientist [Remote-US]

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About Us

Quanata is on a mission to help ensure a better world through context-based insurance solutions. We are an exceptional, customer centered team with a passion for creating innovative technologies, digital products, and brands. We blend some of the best Silicon Valley talent and cutting-edge thinking with the long-term backing of leading insurer, State Farm.

Learn more about us and our work at quanata.com   Our Team   From data scientists and actuaries to engineers, designers and marketers, we’re a world class team of tech-minded professionals from some of the best companies in Silicon Valley, and around the world.  We’ve come together to create the context-based insurance solutions and experiences of the future.  We know that the key to our success isn't just about nailing the technology—it’s hiring the talented people who will help us continue to make a quantifiable impact.

The role

We’re looking for a Data Scientist with a strong engineering and operations mindset to help us build scalable, innovative personal auto risk and telematics models. In this role, you’ll go beyond modeling: you’ll architect robust pipelines, develop libraries used by multiple teams, and champion best practices for version control, testing, and deployment. Reporting to our Data Science Director, you’ll be instrumental in developing new risk models that align with Quanata’s mission to revolutionize transportation risk prediction. Your experience in software engineering and/or data engineering will be crucial as you work alongside a collaborative, cloud-based data science team.

Your day-to-day

1. Architect & Develop: Lead the design, development, and maintenance of advanced personal auto insurance risk models and foundational data pipelines. Create modular, reusable components and libraries that enhance the efficiency and scalability of our modeling process.Technical Leadership: Mentor fellow data scientists and data engineers by encouraging best practices for code structure, version control, CI/CD, testing, and reproducibility. Review pull requests and champion code quality across the data team.
2. Cross-Functional Collaboration: Partner with actuarial, product, and engineering teams to translate complex models and analytical tools into scalable, real-world applications that add measurable business value.
3. End-to-End Ownership: Manage the full project lifecycle—from requirements gathering and architecture to deployment and monitoring—ensuring solutions are robust and optimized for performance in cloud-based environments.
4. Stakeholder Communication: Present analytical findings and operational roadmaps to senior leadership. Demonstrate how sound engineering practices and data-driven insights can inform strategic business decisions.
5. Infrastructure & Tooling: Lead efforts to optimize and automate cloud-based data science environments, establishing guidelines for resource utilization, environment provisioning, and production deployments.
6. Innovation & Continuous Improvement: Stay current with emerging technologies in data engineering, MLOps, and machine learning. Continuously evaluate and integrate new techniques to keep Quanata at the cutting edge of risk prediction.

About you

• Educational Background: Bachelor’s or Master’s degree (or equivalent professional experience) in a quantitative field, Computer Science, or related discipline.
• Relevant Experience: 12 years of combined experience in data science and/or data engineering, with a track record of building and maintaining complex data pipelines or libraries.
• Technical Proficiency: Strong Python and SQL skills, especially in cloud-based environments (AWS, Azure, GCP). Familiarity with software engineering best practices (version control, code reviews, CI/CD, containerization) is critical.
• Independent & Collaborative: Able to drive projects independently from start to finish, yet comfortable working with diverse teams to integrate solutions across the organization.
• Modeling & Analytics: Deep knowledge of machine learning algorithms and data science methodologies, with an ability to deploy models in a production setting.
• Communication: Adept a