About the role.

PURE is seeking a Director, Data Science to help professionalize, scale, and lead our data science practice as a true engineering discipline. This is a foundational role at a pivotal moment: our VP, Applied Sciences has recently joined to grow our data science capability from a handful of models to a production-grade engine influencing decisions across Claims, Underwriting, Risk Management, Distribution, and more. This Director hire will be a critical partner in making that vision real.

This is not a "manage the backlog and report upward" role. It's a builder/path-maker position for someone who is energized by establishing the patterns, frameworks, and habits that make a team consistently excellent - and who can also roll up their sleeves and be one of the principal architects of that work.

What you'll do.

• Architect and enforce a cohesive development standard across the data science team - from exploratory analysis through experimentation, deployment, and ongoing monitoring - so that every model is built on a consistent, reusable foundation rather than in isolation.
• Lead by example as a code-first practitioner, building modular, well-documented Python frameworks and tools that make it dramatically easier for the team to work in consistent patterns and extend prior work without reinventing it.
• Drive experiments and model development with a "fit-for-deployment" mindset from day one - designing solutions in close partnership with Data Engineering, Analytics Engineering, MLOps, IT, and business stakeholders so that what we build can and does make it into the front-end systems where underwriters, claims handlers, risk managers, and sales staff actually do their work.
• Serve as a principal participant in our cross-functional tech lead forum, rapidly estimating effort, shaping high-level designs, and helping build a rigorous but lightweight prioritization and roadmap process - so IDEAS always knows what it could work on, what it should work on, and exactly what is in flight.
• Establish clear success criteria, measurement frameworks, and monitoring standards for every model - both technical (drift, accuracy, bias) and business (KPI achievement, adoption) - because a deployed model that no one is watching isn't really deployed.
• Champion documentation-as-a-habit, not documentation-as-an-afterthought, and help embed that discipline across the team.
• Mentor and elevate colleagues, including more junior data scientists, raising the bar on engineering standards, communication habits, and professional maturity across the team.
• Collaborate deeply with Analytics Engineering to prototype Gold Layer data assets for new models and ensure that no model reaches production on anything less.
• Manage competing priorities with clarity and transparency, helping ensure the team never quietly works on the wrong things and always surfaces tradeoffs early.

What you'll need.

We are looking for a technically exceptional, team-oriented leader who has done this before - not just in theory, but in practice. Someone who has personally felt the pain of a data science team where everyone builds in isolation and then went and fixed it. The ideal candidate will bring:

• 8+ years of experience in applied, code-first data science and analytics within insurance or a closely adjacent industry (financial services, risk, or similar).
• Demonstrated success building and enforcing reusable ML frameworks and engineering standards across a team - Python modules, shared pipelines, consistent documentation patterns, and the discipline to maintain them.
• Hands-on expertise in the full model lifecycle: from problem framing and data exploration through feature engineering, training, validation, deployment into production systems, and continuous monitoring.
• A collaborative instinct - someone who genuinely enjoys working across Data Engineering, Analytics Engineering, MLOps, IT, and business partners to design solutions that are feasible, integrated, and lasting, rather than self-contained.
• Experience in or strong appetite for structured cross-functional work - tech lead forums, lightweight CBAs, roadmap estimation, and the kind of prioritization rigor that keeps a team focused on the highest-value work.
• Strong mentorship instincts, with a track record of raising the technical bar of the people around them - not by telling them what to do, but by building frameworks that make the right way the easy way.
• The intellectual honesty to know when a model isn't ready - to not skip steps, not paper over gaps in the data, and not declare victory before the business is actually using what was built.
• Familiarity with P&C insurance is strongly preferred but not required for the right candidate.

About the team.

PURE Insurance is actively investing in our data, analytics, data science, machine learning (ML), and artificial intelligence (AI) capabilities. We are building a centralized Data & AI department - IDEAS (Innovation in Data Engineering, Analytics & Science) - that brings together specialists across data architecture, engineering, analytics, governance, and advanced modeling. This structure creates extraordinary opportunities for collaboration and impact across every function of the insurance ecosystem, from Claims and Underwriting to Actuarial, Product, Distribution, Finance, and more.

We work with a modern data technology stack that includes AWS, Databricks, dbt, GitHub, Hex, and Arize, while also developing in-house, production-grade software in Python when it creates a genuine competitive edge. At PURE, we embrace curiosity, craftsmanship, and a relentless pursuit of improvement. Our culture values growth, mentorship, transparency, and ownership - and we know that building something durable takes time, discipline, and a willingness to experiment, learn, and adapt.

The base salary for this role can range from $155,000 to $185,000 based on a full-time work schedule. An individual's ultimate compensation will vary depending on job-related skills and experience, geographic location, alignment with market data, and equity among other team members with comparable experience

To ensure a successful onboarding experience, all new hires must work onsite at one of our offices during their first week of employment. Candidates should apply only if they are able to meet this requirement.

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