Company Description
Beige Bananas, the world's fastest-growing AI consulting firm, specializes in developing hyper-custom AI products tailored to rapidly evolving business needs. By leveraging decreasing computation costs and increasing computational power, Beige Bananas focuses on delivering impactful, outcome-driven solutions to help clients maximize value from their analytics investments. The company works with prominent Fortune 500 clients in the Retail, Consumer Packaged Goods (CPG), and Media sectors, building trusted partnerships to foster growth and innovation in AI-driven strategies.
Role Description
This is a full-time remote role for a Lead Data Analyst. The Lead Data Analyst will be responsible for analyzing complex datasets, developing data models, performing statistical evaluations, and providing actionable insights. Additionally, the role involves structuring and optimizing data processes, collaborating with cross-functional teams, and effectively communicating findings for strategic decision-making.
Education & Experience
- Bachelor's Degree in Computer Science, Statistics, Mathematics, Economics, Finance, or a related quantitative field.
- 7+ years of professional experience as a Data Analyst or Business Intelligence Analyst.
- Minimum 2 years of specific experience within the Insurance Industry (Property & Casualty, Life, or Health), with a proven understanding of core insurance data models.
Technical Skills (The Must-Haves)
- Tableau Expertise: Advanced proficiency in Tableau Desktop and Server. Must be capable of creating complex LOD expressions, parameters, and interactive dashboards. Experience with performance optimization and data blending is required.
- Amazon QuickSight Proficiency: Strong hands-on experience with QuickSight, including managing SPICE datasets, creating calculated fields, Row-Level Security (RLS), and setting up email reports/alerts.
- Strong SQL Skills: Expert-level ability to write custom SQL queries to extract, manipulate, and aggregate data from complex data warehouses (e.g., Snowflake, Redshift, or on-premise SQL servers).
- Data Preparation: Experience with data prep tools (e.g., Tableau Prep, Alteryx, or SQL-based transformation) to clean and structure raw data for analysis.
Insurance Domain Knowledge
- Metric Familiarity: Deep understanding of key insurance KPIs such as Gross Written Premium (GWP), Loss Ratios, Combined Ratios, Claims Frequency/Severity, Policy Retention, and Earned Premium.
- Functional Understanding: Familiarity with the data lifecycles of Underwriting, Claims, Actuarial, and Billing functions.
- Regulatory Awareness: Knowledge of data privacy and compliance standards relevant to insurance (e.g., handling PII, GDPR, or HIPAA data) is highly preferred.
