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About This Role
Overview:
Why AAA Life
AAA Life is a respected and trusted American brand that has been focusing on Life Insurance and Annuity Products since 1969\. At AAA Life we have over 1\.8 million policies where we take pride in earning the trust of our policyholders who understand our promise to be there for them – and their families – when we’re needed most. By joining the AAA Life team, you are joining a company that genuinely cares about helping each other, with a devotion to protect the lives of those around us. We embrace a diverse, equitable, inclusive culture where all associates can feel a sense of belonging and use their unique talents and perspective to influence, innovate, motivate, and thrive.
How You’ll Work
Work Solution: Hybrid
Relocation Eligibility: Available
Responsibilities:
What You'll Do:
- Build, maintain, and automate models to predict purchase propensity, policy premium, policy lapse/retention, cross\-selling, upselling, next best action, and other consumer behaviors using both internal data, census data, appended aggregated data, and macroeconomic data. Recommend marketing distribution strategies for leveraging data and models.
- Conduct advanced exploratory data analysis. Perform model interpretability and explainability analysis.
- Leverage specific metrics for model performance evaluation (e.g., precision, recall, F1 score). Implement A/B testing and experimental design and quantitative benchmarks for model improvement
- Apply data privacy and compliance rules under regulations like GDPR, CCPA. Apply ethical AI principles. Apply model fairness and bias mitigation techniques.
- Conduct analyses to assess model performance and campaign performance, both against test datasets and actual results once deployed.
- Forecast campaign results based on models built and validate forecast against actuals.
- Work with marketing data architects and engineers to ensure data is clean, complete, correct, and suitable for modeling using AI/ML platforms.
- Develop and maintain ML pipelines. Implement feature engineering techniques. Find and recommend additional data to use in model building
- Proactively identify opportunities for model improvement and need for additional modeling projects.
- Maintain clear and organized documentation of data, methodologies, and results.
- Implement automation in existing processes to improve overall efficiency.
- Perform ad hoc analysis to support Marketing Distribution efforts
- Actively seek out innovation and optimization use cases and experiments that will result in organizational transformation and sales and profit improvements.
- Utilize advanced analytics and data mining techniques to proactively identify areas of high potential and untapped market opportunities across customer segments, distribution channels, products, and geographies, translating insights into actionable strategies that drive incremental growth and competitive advantage.
- Leverage emerging technologies, including Generative AI and advanced automation tools, to uncover new opportunities for operational efficiency, cost savings, productivity gains, and increased production capacity, while accelerating innovation across modeling, analytics, and marketing workflows.
Qualifications:
Basic Required Qualifications:
- Master’s degree in Statistics, Economics, Mathematics, Data Science or related field. Experienced in marketing analytics or customer behavior modeling.
- 5 to 7 years of experience in data science, including hands\-on experience with Machine Learning (e.g., scikit\-learn, TensorFlow, PyTorch, DataRobot, Databricks), Machine Learning Operations, and Generative Artificial Intelligence. Experience with automated model deployment and monitoring tools.
- Possess outstanding analytical, modeling, problem\-solving, and critical\-thinking skills.
- Experienced with cloud platforms such as AWS, Azure, and Google Cloud. Familiar with big data technologies
- Strong knowledge of machine learning algorithms and their applications in automated systems. Experience with advanced modeling techniques like ensemble methods, time series analysis, and probabilistic modeling
- High proficiency in Python or R for statistical analysis, model development, and process automation. Proficient with SQL for data extraction and manipulation.
- Proficiency with data visualization tools (Power BI, Tableau, or similar) and their automation capabilities
- Skilled in cross\-functional collaboration, agile methodologies, project management, and stakeholder communication.
- Advanced training or academic focus in non\-parametric statistics, resampling methods, or Bayesian approaches for small sample inference
- Experience applying sequential testing or multi\-armed bandit approaches to maximize insights from limited samples in marketing contexts
- Able to effectively communicate and translate complex, technical findings in a candid, clear, concise, and non\-technical fashion to all audiences using Power Point.
- Maintain perspective between the big picture and the tactical details. Remains aligned with the organization’s strategic plan.
- Stellar attention to detail, including maintaining accuracy and consistency across a suite of data science assets, keeping documentation up to date, and proactively identifying and addressing any quality concerns.
- Self\-starter with the ability to identify priorities and focus on items with high business impact.
- Ability to present complex analytical findings with persuasiveness and succinctness
Preferred Experience* Experience in the insurance or financial services industry
- Experience working with Generative AI in the context of predictive modeling
- Knowledge of marketing attribution models, marketing mix media models, automated customer lifetime value analysis, and customer lapse prediction.
- Experience with automated MLOps and continuous model deployment
- Experience with Git for version control and automated CI/CD for analytical code
- Understanding of risk assessment and actuarial modeling principles
While performing the duties of this job, the employee is frequently required to stand, walk, sit, use hands to finger, handle, or feel, talk, hear and concentrate. Specific vision abilities required by this job include close vision, distance vision, depth perception, and ability to adjust focus.
This job requires the ability to perform duties contained in the job description for this position, including, but not limited to, the above requirements. Reasonable accommodation will be made for otherwise qualified applicants as needed to enable them to fulfill these requirements.
We are committed to ensuring equal employment opportunities for all job applicants and employees. Employment decisions are based upon job\-related reasons regardless of an applicant's race, color, religion, sex, sexual orientation, gender identity, age, national origin, disability, marital status, genetic information, protected veteran status, or any other status protected by law.
AAA Life Insurance Company does not offer immigration sponsorship for this position. This includes visa types such as H\-1B, TN, and STEM OPT. Please do not apply if you currently require or may require employer\-sponsored immigration support now or in the future.
\#LI\-Hybrid
Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 26,159 AI roles we're tracking, Data Scientist positions make up 2% of the market. At AAA Life Insurance Company, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $204,700 based on 441 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
AAA Life Insurance Company AI Hiring
AAA Life Insurance Company has 1 open AI role right now. They're hiring across Data Scientist. Based in Livonia, MI, US.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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