Senior Data Scientist - Modeling and Analytics

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