Senior Data Analyst @ Progressive Leasing
Your Application Journey
Email Hiring Manager
Job Details
About Progressive Leasing
Progressive Leasing is a leading provider of in-store and e-commerce lease-to-own solutions. With almost 20 years in the FinTech sector, they have evolved from a start-up to an industry leader, innovating and simplifying processes while valuing all people.
Role Overview - Senior Data Analyst
This remote role is ideal for a driven analytics professional responsible for drawing business insights through complex analysis, data mining, and visualization. You will work with cutting edge tools and models to drive significant business impact.
Key Responsibilities
- Create interactive data visualizations for business leaders.
- Build and run models for optimal resource allocation.
- Analyze transaction patterns to prevent fraud.
- Extract actionable insights from unstructured data using advanced data mining techniques.
Requirements
Applicants should hold a Bachelor’s degree or have relevant work experience. Proficiency with data mining and visualization tools such as R, Python, Tableau, or Power BI, as well as querying languages like SQL, is essential. A track record of delivering business results through innovative analysis is required. Occasional travel (approximately 10%) may be needed.
What We Offer
- Competitive Compensation
- Opportunities for advancement in data science and leadership careers
- Full Health Benefits including Medical/Dental/Vision/Life and Paid Parental Leave
- Company Matched 401k, Employee Stock Purchase Program and Tuition Reimbursement
- Paid Time Off, Holidays, and Volunteer Hours
Key skills/competency
- Data Analysis
- Data Mining
- Visualization
- SQL
- Python
- Tableau
- R
- Problem Solving
- Modeling
- Big Data
How to Get Hired at Progressive Leasing
🎯 Tips for Getting Hired
- Customize your resume: Emphasize data analytics and visualization skills.
- Study Progressive Leasing: Research company culture and FinTech trends.
- Highlight relevant tools: List experience with SQL, Python, Tableau.
- Prepare detailed examples: Share past analytical problem-solving successes.