Data Quality & Purging Analyst @ QBE Insurance
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Job Details
Overview
The role of Data Quality & Purging Analyst at QBE Insurance applies scientific principles and data-driven analytics to improve business unit performance. The candidate will translate stakeholder needs into analytically tractable problems, utilize scientific methods and big data tools, and communicate insights effectively.
Responsibilities
- Develop and refine analytical use cases with business customers.
- Engage in diverse projects within the insurance domain.
- Translate between business requirements and analytical problems.
- Analyze and interpret data to test and iterate hypotheses.
- Create data visualizations to present results and recommendations.
- Support Data & Analytics teams as a machine learning expert.
- Lead onshore and offshore analytics and data science projects.
- Plan sprint activities and accurately estimate effort.
- Ensure compliance with QBE policies, procedures and relevant legislation.
- Share knowledge and promote reusable practices across teams.
Work Experience & Qualifications
Necessary work experience includes some relevant experience, with a preference for candidates experienced with large datasets, R, Python, Spark, and Linux environments. A tertiary degree or equivalent is required, with preference given to candidates with degrees in Computer Science, Engineering, Statistics, Mathematics or related quantitative fields.
Additional Information
This full-time position requires occasional travel (1-4 trips annually) and typical office-based physical demands. QBE Insurance is an equal opportunity employer and complies with all relevant EEO legislation.
Key skills/competency
- Collaboration Tools
- Communication
- Critical Thinking
- Data Analysis
- Data Science
- Data Visualization
- Economics
- Machine Learning
- R Programming
- Stakeholder Management
How to Get Hired at QBE Insurance
🎯 Tips for Getting Hired
- Research QBE Insurance's culture: Understand mission, values, and latest news.
- Customize your resume: Highlight data science and analytics experience.
- Prepare technical examples: Showcase projects using R, Python, and Spark.
- Review case studies: Familiarize with insurance analytics challenges.