Senior Data Scientist - Modeling and Analytics

AAA Life Insurance Company•Livonia, MI

12h•Hybrid

About The Position

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.

Requirements

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.

Nice To Haves

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

Responsibilities

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.