About the job
Who is Credible?
Credible is a dynamic marketplace empowering consumers to compare personalized, prequalified rates and quotes from various lenders and carriers for student loans, mortgages, personal loans, and insurance.
We are committed to challenging the traditional lending landscape, advocating for a system where ethical lending and insurance practices flourish. Our innovative platforms are designed to truly serve our customers' needs, ensuring they find the best loan or insurance policy available.
At Credible, we believe that the process of researching and purchasing loans or insurance should be straightforward and user-friendly, which is why we prioritize simplicity in our offerings.
About the Role:
We are seeking a talented and analytical Data Analyst to join our Business Intelligence and Analytics team. This pivotal role, aligned with the Insurance team, is essential for delivering insights that guide strategic decisions across multiple business functions, including user acquisition, brand marketing, product development, finance, and operations. This is an excellent opportunity to join a growing company and play a key role in enhancing our products and improving customer experiences.
Responsibilities:
- Collaborate with the Insurance team to identify analytics needs and develop dashboards and analytical solutions that enhance operational efficiency.
- Engage with business leaders to define key performance metrics and create reports to track these metrics.
- Identify data deficiencies and coordinate with Engineering to resolve these issues.
- Work with stakeholders to outline requirements for data models in our data warehouse, facilitating effective and intuitive analyses.
- Conduct thorough investigative data analyses that yield actionable insights, managing the process from idea generation to execution and presentation of findings.
- Assist in structuring A/B tests, setting goals, and determining measurement and logging requirements for tests and features.
- Effectively collaborate with Web, QA, and Data Engineers to operationalize data.
