Senior Data Analyst
Bluevine
Job Overview
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Job Description
About Bluevine
Bluevine is the largest small business banking platform in the U.S., redefining how entrepreneurs manage their money. We create modern financial solutions, from checking and lending to payments and beyond, designed to help small business owners grow, thrive, and take control of their financial future. Our best-in-class technology, advanced security, and deep understanding of the small business community give our customers the confidence to focus on what they do best.
Since 2013, we’ve supported more than 750,000 small businesses nationwide. Backed by leading investors like Lightspeed Venture Partners, Menlo Ventures, 83North, and Citi Ventures, our 500+ person global team shares one mission: to give small businesses the financial tools they need to succeed.
We’re innovators driven by big ideas, collaboration, and real impact. Here, you’ll have the freedom to take ownership, grow your career, and make a difference for small business owners across America. Ready to shape what’s next?
Join our growing Credit team and help shape how we manage credit risk while supporting Bluevine’s mission and business goals.
You’ll play a key role in developing the credit domain, ensuring we stay within our risk appetite and enable smart, sustainable growth.
What You’ll Do as a Senior Data Analyst
- Lead meaningful projects focused on credit offers and risk strategies, balancing risk mitigation with product growth.
- Dive deep into data to identify high-risk customers and propose thoughtful, data-driven solutions to manage them.
- Design and build automated, end-to-end decision processes using Python and internal tools to reduce risk efficiently.
- Clearly communicate your findings and methods to both technical and non-technical stakeholders across the company.
- Collaborate closely with teams across Bluevine – including Data Science, Data Engineering, and Risk Operations – to make sure our credit strategies are aligned and effective.
What We Look For
- Bachelor’s degree in a quantitative field such as Statistics, Computer Science, Software Engineering, Industrial Engineering, Mathematics, or Economics
- 3+ years of experience in an analytical or data-driven role
- Hands-on experience (2+ years) working with Python for quantitative analysis
- Advanced SQL skills
- Strong communication skills in English, both written and verbal
- Strong analytical mindset with a technical orientation
- Proactive, detail-oriented, and curious
- A great team player who communicates clearly and enjoys working with others
- Ability to turn data into actionable insights and present them to diverse stakeholders
Bonus Points
- Experience developing statistical or stochastic models
- Experience working with BI tools like Tableau, Power BI, or similar
- Experience with process automation
- Knowledge of financial market
- Experience in the credit domain
Benefits & Perks
- Excellent group health coverage
- Stock options
- Flexible hybrid work model
- Large Study Fund contribution
- Salary Benchmarks and Checkpoints
- Monthly meal card of TenBis or CiBus (your choice) with generous balance
- Free parking for cars, scooters, and bikes
- Free gym membership
- Company-sponsored Mental Health Benefits
- PTO, Company Holidays, and Flexible Holidays
- Community-based volunteering opportunities
Key skills/competency
- Data Analysis
- Credit Risk Management
- Python Programming
- Advanced SQL
- Stakeholder Communication
- Process Automation
- Quantitative Analysis
- Problem-Solving
- Risk Mitigation
- Data-Driven Strategy
How to Get Hired at Bluevine
- Research Bluevine's culture: Study their mission, values, recent news, and employee testimonials on LinkedIn and Glassdoor to understand their FinTech innovation.
- Tailor your resume: Highlight extensive experience with Python, advanced SQL, credit risk management, and data-driven solutions relevant to financial services.
- Showcase quantitative skills: Emphasize your ability to transform complex data into actionable insights and your strong analytical mindset.
- Prepare for technical challenges: Expect rigorous SQL and Python coding exercises, and be ready to discuss statistical or stochastic modeling concepts.
- Demonstrate collaboration: Provide examples of successful cross-functional projects, especially with Data Science, Engineering, and Risk Operations teams.
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