Director, Data Science and Marketing Analytics Innovation

AAA Life Insurance Company•Livonia, MI

41d•Hybrid

About The Position

The Data Science and Marketing Analytics Innovation Lead drives data-driven marketing strategies for a specific product line. This agile, empathetic, adaptable analytics leader bridges marketing and data science teams while demonstrating deep data science marketing knowledge—including direct mail, digital strategies, customer behavior, campaign execution and optimization, and process automation —to develop effective, data-driven decisions. Excelling at multitasking, they seamlessly transition between strategic planning and tactical execution under tight deadlines, effectively communicating complex concepts to diverse audiences. They contribute to the organizational transformation initiatives, lead change management, foster a culture of data-driven decision-making, identify and implement emerging analytics technologies. This role supervises a team of data scientists, analysts, and engineers and collaborates with product team members and managers.

Requirements

Previous experience in the life insurance, insurance, or adjacent industry
Experience with Generative AI technologies for marketing applications
Experience leading cross-functional teams in a matrix organization without direct authority
Experience with customer journey and lifetime value analysis
Master's degree in Statistics, Data Science, Economics, Computer Science, Mathematics, or related quantitative field
Minimum 10 years of experience in data science, analytics, or related field
Minimum of 7 years of experience in managing and leading people
Strategic thinker with a passion for innovative marketing strategies; has a deep understanding of direct mail and digital marketing analytics. Proven leadership in data science or analytics with a marketing focus.
Strong technical background in predictive modeling, statistics, and data visualization coupled with experience leading teams through organizational transformation and innovation.
Extensive hands-on experience in direct marketing, digital campaign optimization, experimental design, A/B testing, modeling using Generative AI/ML, and marketing media attribution models.
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
Advanced proficiency in Python, SQL, and marketing technology platforms such as DataRobot, Adobe Campaigns, DataBricks, and CDPs. Experience with marketing technology cloud platforms (AWS, Azure, GCP) and data visualization tools (Tableau, Power BI, or similar). Adaptable and quick with technology.
Knowledge of automation tools and technologies (workflow automation, RPA, AI-driven automation). Experience designing and implementing automated reporting and alerting systems.
Excellent persuasive communication skills for bridging technical and non-technical audiences. Excellent presentation skills for executive leadership.
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 accommodations will be made for otherwise qualified applicants as needed to enable them to fulfill these requirements.

Responsibilities

Product Data and Analytics Strategy: Develop and execute data and analytics plans aligned with product and organizational goals.
Predictive Modeling: Lead the creation of customer segmentation and propensity models to enhance marketing effectiveness.
Marketing Analytics: Direct cross-channel campaign analysis, establish KPIs, and optimize marketing ROI.
Cross-functional Collaboration: Serve as the primary liaison between data teams and marketing stakeholders, translating technical insights into actionable business recommendations.
Team Leadership: Mentor and manage Data Scientists, Analysts, Engineers in a collaborative, innovative environment.
Data Infrastructure: Collaborate with Analytics Engineering to build and maintain robust, compliant data pipelines.
Experimental Design: Implement testing frameworks and A/B experiments to boost customer experience and marketing performance.
Performance Reporting: Develop dashboards and reports to monitor product performance and key business metrics.
Automation Strategy: Spearhead the identification, design, and implementation of automation opportunities, including generative AI, across the data and marketing workflow. Develop intelligent automation solutions that reduce manual effort, increase operational efficiency, and allow the team to focus on high-value strategic activities.
Innovation and Optimization: Review and understand tools in the marketplace that will help with innovation and optimization. Actively seek out new use cases and experiments that will result in organizational transformation and sales and profit improvements.