Contribute to the development and implementation of predictive analytics to support key business decisions
Apply statistical and analytical techniques to build models and generate actionable insights
Develop and maintain data pipelines, ETL processes, and data transformations to support analytics workflows
Perform data extraction, preparation, and integration across multiple sources, ensuring data quality and reliability
Use SQL and programming tools (Python, R) to analyze large datasets and create analytical solutions
Evaluate model performance and contribute to ongoing enhancements and optimization
Design reports and visualizations to communicate insights and track performance metrics
Conduct ad-hoc analyses to support emerging business questions and priorities
Partner with business stakeholders and communicate findings effectively to both technical and non-technical audiences
Build strong understanding of the enterprise data landscape, including finance and insurance data domains (pricing, underwriting, claims)
Contribute to continuous improvement of analytics processes, tools, and methodologies
Operate with a high degree of autonomy, proactively building relationships and driving initiatives forward in a fast-evolving environment

3+ years of experience in data analytics, predictive analytics, or a related field
Bachelor’s degree in a quantitative field (e.g., Mathematics, Statistics, Computer Science, Engineering or similar); Master’s degree is a plus
Background in insurance industry is strongly preferred (pricing, underwriting, claims), but not mandatory
Actuarial knowledge or qualification is a plus
Strong hands-on experience with:
SQL (advanced)
ETL and data pipeline development (advanced)
Working knowledge of:
Python and/or R (intermediate)
Familiarity with data warehousing solutions such as Snowflake is a plus
Understanding of predictive modeling concepts and statistical analysis
Strong analytical thinking and attention to detail
Ability to work independently with minimal guidance in an unstructured, evolving environment
Curiosity and ownership to understand data sources, systems, and business context end-to-end
Excellent communication skills, with the ability to clearly present insights and “tell the story behind the data”
Relationship-building mindset, proactively engaging stakeholders across functions
Adaptability and comfort working in a fast-paced, startup-like environment within a larger organization
Experience working with finance or enterprise datasets is a plus
Exposure to executive-level presentations or stakeholder engagement is highly valued.

Predictive Analyst II

Key skills