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About This Role
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Job Title
Senior Data Science Consultant, Insurance \- RemoteRequisition Number
R7828 Senior Data Science Consultant, Insurance \- Remote (Open)Location
Arizona \- Home TeleworkersAdditional Locations
Alabama \- Home Teleworkers, Alabama \- Home Teleworkers, Arkansas \- Home Teleworkers, California \- Home Teleworkers, Colorado \- Home Teleworkers, Connecticut \- Home Teleworkers, Delaware \- Home Teleworker, District of Columbia \- Home Teleworkers, Florida \- Home Teleworkers, Georgia \- Home Teleworkers, Idaho \- Home Teleworkers, Illinois \- Home Teleworkers, Indiana \- Home Teleworkers, Iowa \- Home Teleworkers, Kansas \- Home Teleworker, Kentucky \- Home Teleworkers, Louisiana \- Home Teleworkers, Maine Home Teleworkers, Maryland \- Home Teleworkers, Massachusetts \- Home Teleworkers, Michigan \- Home Teleworkers, Minnesota \- Home Teleworkers, Mississippi \- Home Teleworker, Missouri \- Home Teleworker, Montana \- Home Teleworkers {\+ 21 more}Job Information
CSAA Insurance Group (CSAA IG), a AAA insurer, is one of the leading personal lines property and casualty insurance groups in the United States. Here, every employee shapes our mission. We build innovative, human\-centered solutions that help AAA members prevent, prepare for, and recover from life's uncertainties. You will join a collaborative, inclusive culture where your strengths have room to grow and your ideas can drive real impact. Step into a role where you can contribute to our shared success through meaningful work.
We are actively hiring for a Senior Data Science Consultant, Insurance \- Remote
Your Role:
This Data Scientist role offers the opportunity to help CSAA Insurance Group anticipate and prepare for the future of the insurance industry. In this highly strategic position, you will build and apply advanced simulation models to explore emerging trends and their potential impact over the next decade. Your work will help leaders evaluate scenarios, test assumptions, and make informed decisions about how CSAA can adapt, grow, and continue serving members in a rapidly changing marketplace.
This role is ideal for a seasoned data science professional with deep expertise in modeling, simulation, AI, data analysis, and programming, along with the ability to translate complex findings into clear business recommendations. You will work closely with senior leaders and cross\-functional teams to design simulation frameworks, interpret results, communicate assumptions and limitations, and influence decisions that may shape CSAA’s long\-term strategy. Beyond delivering high\-impact analytics, this person will help strengthen CSAA’s internal simulation capabilities by mentoring others and advancing the broader data science community.
Your Work:
- Lead the design, development, and validation of simulation models (discrete\-event, agent\-based, system dynamics, or hybrid).
- Translate business or operational problems into robust simulation frameworks.
- Conduct scenario analysis, what\-if studies, optimization, and risk analysis.
- Interpret simulation results and large datasets, delivering actionable insights to executive and technical audiences to support decision\-making.
- Guide business users on model assumptions, limitations, and best practices.
- Coach and mentor junior consultants and review modeling work for quality and rigor.
- Support problem understanding and solution design, including proposals and executive presentations.
- Partner closely with data governance, model governance, and regulatory compliance teams.
- Key partner to the business, advising and making actionable recommendations with significant impact.
- Play key roles cross\-functionally with data, IT, governance, legal, and business.
- Take a leading role in shaping the culture of data science community in CSAA.
Required Experience, Education and Skills:
- Bachelor’s degree in STEM or bachelor’s degree with an equivalent combination of relevant education.
- Requires 10 years related professional experience with transferrable skills.
- Experience in developing data pipelines for simulations and data science models.
- Experience with fostering the culture of data scientist community.
- Master\-level tool knowledge (e.g., SQL, Python, AWS, DataRobot, AnyLogic, and other simulation tools).
- Deep knowledge in AI, data mining, data evaluation, and proficiency in compiling an effective and comprehensive modeling dataset.
- Strong communication skills to explain model insights to technical partners, business partners, and corporate leaders.
- Master\-level data science industry knowledge and knowledge of the insurance marketplace.
What would make us excited about you?
- Master's degree
- CPCU, ACAS or FCAS
- Extensive knowledge of various insurance operations: pricing, product, claims, marketing, and underwriting
- History of thought leadership in modeling, including publishing, speaking at conferences, or contributing to industry collaborations.
- Considered a leader in the field of modeling with a history of publishing articles and books, contributing to research and speaking at conferences and webinars.
- Actively shapes our company culture (e.g., supporting employee resource groups, mentoring employees, volunteering, joining cross\-functional projects).
- Champions our cultural norms (e.g., willing to have cameras when it matters: helping onboard new team members, building relationships, etc.).
- Demonstrates a company ownership mindset, thinking beyond boundaries of their own area.
- Travels as needed for role, including divisional / team meetings and other in\-person meetings.
- Fulfills business needs, which may include investing extra time, helping other teams, etc.
Please note we are hiring for this role remote anywhere in the United States with the following exceptions: Hawaii and Alaska.
Why Choose a Career at CSAA IG?
At CSAA IG, we are a mission\-driven organization proudly committed to empowering our members, our employees, and our communities to thrive.
Recognition: We offer a total compensation package, annual bonus eligibility for most roles, 401(k) with a company match, and so much more! Read more about what we offer and what it is like to be a part of our dynamic team at https://careers.csaainsurance.aaa.com/us/en/benefits.
Career Growth: We believe in growth for everyone. Here at CSAA IG, leaders and mentors partner with employees to align interests, unlock development opportunities, and support long‑term success.
Flexible Workplace: We embrace a remote\-first culture through our Flexible Workplace. Most employees hold Home\-Flex roles, working primarily from home, often with the flexibility to work from various locations including CSAA offices. Our flexible workplace empowers you to balance remote work with intentional in‑person moments that deepen connection and collaboration.
Inclusion and Belonging: An inclusive and welcoming workplace is the cornerstone of our success. By fostering an environment where people feel valued and heard, we deepen our ability to understand and meet the unique needs of our members. This strengthens innovation and enhances our products and services, giving us a competitive edge in the market.
Sustainability: As climate change leads to more frequent and severe weather events, we are taking bold action to build more resilient communities and reduce our environmental impact. Submit your application to be considered. We communicate via email, so check your inbox and/or your spam folder to ensure you don’t miss important updates from us.
CSAA is committed to providing reasonable accommodations to qualified applicants and employees with disabilities or other limitations. If you would like to request an accommodation to participate in the job application or interview process, please contact [email protected]
If you apply and are selected to continue in the recruiting process, we will schedule a preliminary call with you to discuss the role and will disclose during that call the available salary/hourly rate range based on your location. Factors used to determine the actual salary offered may include location, experience, or education.
CSAA does not provide visa sponsorship for this role. Applicants must have authorization to work indefinitely in the US. Please do not apply for this role if at any time (now or in the future) you will need immigration support (i.e., H\-1B, TN, STEM OPT Training Plans, etc.).
CSAA Insurance Group is an equal opportunity employer.
\#LI\-JH1
Knowledge, Skills and Abilities
Required:
- Master\-level tool knowledge (e.g., SQL, Python, R, AWS, DataRobot, other systems)
- Depth knowledge data mining, data evaluation and proficiency in compiling an effective and comprehensive modeling dataset
- Create innovative ways to mine and use data, including semi\-structured or unstructured data
- Master\-level statistical, ML, GenAI and LLM modeling skills through development, deployment, monitoring
- Key partner with data governance, model governance and regulatory compliance
- Deep understanding of responsible AI principles for GenAI, including governance, compliance, legal, and ethical considerations; ability to influence internal policy around GenAI
- Key partner to the business, advising and making actionable recommendations with significant impact
- Play important role cross\-functional with data, IT, governance, legal and business
- Take a leading role in shaping the culture of data science community in CSAA
- Master\-level data science industry knowledge and knowledge of the insurance marketplace
- Strong communication skills to explain model insights to technical partners, business partners and corporate leaders
- Considered a leader in the field of modeling with a history of publishing articles and books, contributing to research and speaking at conferences and webinars
Education
Required
- Bachelor’s degree in STEM or Bachelor’s degree with an equivalent combination of relevant education
Preferred:
- Master's degree
- CPCU, ACAS or FCAS
Experience
Required:
- Requires 10 years related professional experience with transferrable skills
- 7 years of post\-bachelor experience developing statistical, ML, GenAI and LLM Models
- Lead cross\-functional MLOps projects in insurance or financial services
- Experience with developing GenAI models with unstructured data
- Experience with fostering the culture of data scientist community
Preferred
- Extensive knowledge in various insurance operations: pricing, product, claims, marketing and underwriting
- Experience shaping GenAI adoption across company
- Track record of thought leadership in GenAI, including publishing, speaking at conferences, or contributing to industry collaborations
- Familiarity with responsible AI frameworks and ability to influence internal processes and standards related to GenAI adoption
The national average salary range for this position is $160,155\.00\-$177,950\.00\. However, we have a location\-based compensation structure. Our salary ranges vary and are calculated based on work location. The starting pay range for this position across all the states we hire in is $160,155\.00\-$213,550\.00\. This role also includes an opportunity for a company\-wide annual discretionary bonus, through our Annual Incentive Plan (AIP), of up to 15% of eligible pay.
This job posting will be unposted on Sat, 15 Aug 2026\.
Salary Context
This $160K-$213K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At CSAA Insurance Group, a AAA Insurer, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($186K) sits 15% below the category median. Disclosed range: $160K to $213K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
CSAA Insurance Group, a AAA Insurer AI Hiring
CSAA Insurance Group, a AAA Insurer has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $213K - $213K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 Safety roles lead at $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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