You'll enjoy the flexibility to work remotely * from anywhere within the U.S. as you take on some tough challenges. For all hires in the Minneapolis or Washington, D.C. area, you will be required to work in the office a minimum of four days per week.
Primary Responsibilities:- Serve as a senior analytics partner to the Credit team, providing data-driven insights across credit risk, portfolio performance, underwriting, and loss mitigation
- Develop, optimize, and maintain complex SQL queries to extract, transform, and analyze large volumes of financial and credit data from enterprise data warehouses
- Leverage Python (e.g., pandas, NumPy) to perform advanced data analysis, automation, validation, and feature engineering, complementing SQL-based workflows and improving analytical efficiency
- Design, build, and support interactive dashboards and reports in Power BI to deliver clear, actionable insights to Credit leadership and business stakeholders
- Create and maintain SSRS reports to support operational, regulatory, and management reporting needs, ensuring accuracy, consistency, and timeliness
- Perform advanced analysis using Excel, including pivot tables, Power Query, Power Pivot, and complex formulas, to support ad hoc requests and deep-dive investigations
- Analyze credit metrics such as delinquency, roll rates, charge-offs, recoveries, exposure, and vintage performance to identify trends, risks, and opportunities
- Partner closely with Credit Risk, Underwriting, Finance, and Compliance teams to ensure reporting aligns with business rules, policies, and regulatory expectations
- Validate data integrity and reconcile results across multiple systems to ensure reporting accuracy and reliability
- Translate complex analytical findings into clear, concise insights and recommendations tailored to both technical and non-technical audiences
- Support automation and process improvements to increase efficiency, scalability, and self-service analytics within the Credit organization
- Mentor junior analysts and contribute to the development of best practices for SQL, reporting standards, and analytical methodologies
- Ensure adherence to data governance, security, and regulatory requirements specific to banking and credit data
You'll be rewarded and recognized for your performance in an environment that will challenge you and give you clear direction on what it takes to succeed in your role as well as provide development for other roles you may be interested in.
Required Qualifications:- Bachelor's degree in Data Analytics, Finance, Economics, Statistics, Information Systems, or a related field, or equivalent practical experience
- 5+ years of experience in data analytics, with direct experience supporting Credit, Credit Risk, or Lending teams within a bank or financial services organization
- Advanced experience with Power BI, including data modeling, DAX, custom measures, and designing executive-ready dashboards
- Hands-on experience developing, maintaining, and supporting SSRS reports in a production environment
- Solid working knowledge of credit concepts, including delinquency, charge-offs, recoveries, exposure, vintages, utilization, and portfolio performance
- Advanced Python proficiency, with hands-on experience using libraries such as pandas, NumPy, and related analytical packages for data manipulation, validation, automation, and large-scale analysis; experience building reusable scripts, analytical frameworks, or pipelines is highly valued
- Expert-level proficiency in SQL, with experience writing complex joins, CTEs, subqueries, window functions, and performance-optimized queries against large datasets
- Proven advanced Excel skills, including pivot tables, Power Query, Power Pivot, complex formulas, and statistical or financial analysis
- Proven ability to validate data, reconcile across multiple source systems, and ensure high data quality and reporting accuracy
- Proven solid analytical and problem-solving skills, with the ability to independently investigate issues and deliver insights
- Proven excellent communication skills with the ability to explain complex data findings to both technical and non-technical stakeholders
Preferred Qualifications:- Experience working with consumer or commercial lending products (e.g., credit cards, auto loans, personal loans, mortgages, or commercial loans)
- Experience working with large enterprise data warehouses and financial systems
- Experience building automated or self-service reporting solutions for business users
- Experience leveraging Python for automation, process optimization, or advanced analytics (e.g., trend analysis, scenario analysis, or custom performance monitoring)
- Experience developing, validating, or deploying analytical or statistical models using Python, including feature engineering, model evaluation, and performance monitoring in a financial or risk analytics context
- Experience supporting senior leadership with executive-level reporting and insights
- Familiarity with banking regulatory and risk frameworks (e.g., CECL, stress testing, portfolio monitoring, audit support)
- Demonstrated ability to mentor junior analysts and establish analytics best practices
*All employees working remotely will be required to adhere to UnitedHealth Group's Telecommuter Policy
Pay is based on several factors including but not limited to local labor markets, education, work experience, certifications, etc. In addition to your salary, we offer benefits such as, a comprehensive benefits package, incentive and recognition programs, equity stock purchase and 401k contribution (all benefits are subject to eligibility requirements). No matter where or when you begin a career with us, you'll find a far-reaching choice of benefits and incentives. The salary for this role will range from $91,700 to $163,700 annually based on full-time employment. We comply with all minimum wage laws as applicable.
Application Deadline: This will be posted for a minimum of 2 business days or until a sufficient candidate pool has been collected. Job posting may come down early due to volume of applicants.
