Senior Manager

Data Scientist – Roles and Responsibilities

We are seeking an experienced Senior Data Scientist to lead advanced analytics initiatives focused on marketing strategies for our insurance carrier. The ideal candidate will have deep expertise in marketing analytics, leveraging data to optimize customer acquisition, retention, and segmentation within the insurance industry (e.g., Life & Annuity, Property & Casualty). You will drive data-driven decision-making by developing sophisticated models, collaborating with cross-functional teams, and delivering actionable insights to enhance marketing performance and business outcomes.

Key Responsibilities

1. Marketing Analytics: Design and implement advanced analytical models to optimize marketing campaigns, customer segmentation, and lifetime value (CLV) prediction for insurance products.
2. Customer Segmentation: Apply unsupervised learning techniques (e.g., K-Means, Hierarchical Clustering) to identify high-value customer segments using internal and external data sources.
3. Predictive Modeling: Develop and deploy machine learning models for churn prediction, cross-selling opportunities, and personalized marketing recommendations, ensuring alignment with insurance underwriting and pricing strategies.
4. Campaign Optimization: Analyze marketing campaign performance (e.g., email, digital ads, direct mail) using A/B testing and attribution modeling to maximize ROI and customer engagement.
5. Data Integration: Collaborate with data engineers to integrate diverse datasets, including policy administration systems (e.g., policy details, claims data), CRM platforms, and external sources (e.g., consumer purchase data, geospatial information) for comprehensive analysis.
6. Visualization & Reporting: Create intuitive dashboards and visualizations using tools like Power BI, Tableau, or Python libraries (e.g., Seaborn, Plotly) to communicate insights to marketing, underwriting, and executive stakeholders.
7. Cross-Functional Collaboration: Partner with marketing, actuarial, and product teams to align analytics with business objectives, such as improving customer retention or targeting underserved markets.