Senior Data Analytics: Loss Forecasting & Modeling

U.S. Bank; United States; Remote; USD 90,000 - 130,000; Full time

Benefits offered by this job

Healthcare

401(k) retirement plan

Paid vacation

11 paid holidays

Job summary

U.S. Bank is seeking a data analytics professional to join the Loss Forecasting team within Consumer and Business Banking. The role focuses on forecasting analytics, model risk governance, and interpreting results to support credit and forecasting decisions.

The ideal candidate has strong analytical skills, experience with credit loss forecasting, and proficiency in SQL, Python, and R. Banking experience and collaboration across teams are valued for delivering actionable insights.

Qualifications

• Bachelor's degree in a related field or equivalent work experience.
• Five to seven years of statistical and/or data analytics experience.
• Experience with credit loss forecasting and forecast analysis.
• Experience in banking/financial services, particularly within credit, finance, forecasting, modeling, or credit strategy.

Responsibilities

• Working on big data/analytics projects that gather and integrate large volumes of data, perform analysis and develop actionable insights.
• Acquires data from multiple data sources to perform analysis.
• Identifies, analyzes and interprets trends or patterns to provide answers and recommendations.
• Interprets data and analyzes results using various statistical techniques and tools.
• Presents data and analysis clearly for quick understanding and data-driven decisions.
• Collaborates with partners to provide a holistic view of the analysis.

Skills

SQL

Python

R

Excel

Hadoop

SAS

SPSS

Forecasting

Model Risk Governance

Education

Bachelor's degree or equivalent

Tools

Geospatial tools

Job description

U.S. Bank is seeking a data analytics professional to join the Loss Forecasting team within Consumer and Business Banking. The role focuses on forecasting analytics, model risk governance, and interpreting results to support credit and forecasting decisions.

The ideal candidate has strong analytical skills, experience with credit loss forecasting, and proficiency in SQL, Python, and R. Banking experience and collaboration across teams are valued for delivering actionable insights.