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About This Role
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.
What We Do
We're a member\-owned property and casualty insurer designed exclusively for financially successful families and driven by a purpose of doing what is right for our members. We provide exceptional service, hospitality and care, we partner with our members to help prevent losses and we create smart insurance solutions at fair prices.
We aim for our members to *love their insurance* . It is our mission is to create a membership experience so compelling that our members never want to leave.
Who We Are
We want to be transparent about what we expect from each other. From PURE, you can expect:
*Opportunities to stretch and grow:* your professional and personal development matters to us. We’re committed to providing experiences through on\-the\-job learning and professional development that increase your impact and rewards.
*Clarity and kindness:* you can rely on us to be open, honest and supportive, offering clarity on what success looks like.
*Support in good times and bad:* we believe in showing up for each other consistently, not only when it’s easy. You can expect a thoughtful partner, even when we disagree.
*A community that cares:* we are committed to sustaining a community in which each person feels cared for as an individual. We lift each other up, celebrate wins together and support one another through challenges in work and life.
Who You Are
All of the strongest relationships are a partnership\- a two way street. So here’s what we ask of you:
- Aim to bring your best every day: you’re here because you want to be part of a team that makes a real impact and aims high.
- Be a student and a teacher: share your knowledge and talents and be willing to listen and learn from those around you.
- Get comfortable being uncomfortable: we face tough moments and obstacles with a “courage over comfort” approach and a positive, solutions\-oriented mindset.
- Be a culture builder: building a positive culture is everyone's responsibility, based on care, respect and openness to diverse perspectives.
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.
Salary Context
This $155K-$185K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At PURE Insurance, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $181,170 based on 12,692 positions with disclosed compensation. Director-level AI roles across all categories have a median of $247,800. This role's midpoint ($170K) sits 6% below the category median. Disclosed range: $155K to $185K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
PURE Insurance AI Hiring
PURE Insurance has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $185K - $185K.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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