Data Scientist I — Credit Risk & Growth Analytics

Bank of America; Atlanta (GA); On-site; USD 120,000 - 170,000; Full time

Job summary

Bank of America is seeking a Data Scientist to analyze large datasets and develop risk management strategies. You will collaborate with stakeholders to translate business problems into analytical solutions and present findings to drive responsible growth.

Responsibilities include building predictive models for credit risk and fraud analytics, creating dashboards, and ensuring data quality while working across data platforms and teams in a fast-paced environment.

Qualifications

• Master's degree or higher in a quantitative field.
• Strong STEM background with ML, NLP, statistics, forecasting.
• At least 2 years of Tableau data visualization experience.
• Proficiency in Python, SQL, SAS, R and MATLAB; familiarity with distributed systems.
• Excellent written and verbal communication; strong business analysis.

Responsibilities

• Develop analytical solutions for credit risk, fraud analytics, and portfolio management.
• Extract, transform, and analyze large enterprise datasets.
• Build dashboards, reports, and automated analytical workflows.
• Support risk scoring, monitoring, and stress testing initiatives.
• Ensure data quality, validation, and process improvements.
• Conduct portfolio analytics, segmentation, and loss forecasting.
• Incorporate third-party data when needed.

Skills

Adaptability

Attention to Detail

Communication

Education

Master's degree in Computer Science/Data Science/Statistics/Mathematics/Finance

Tools

Python

SQL

SAS

R

MATLAB

Tableau

NoSQL

Pandas/Numpy

Excel

C/C++

Job description

Bank of America is seeking a Data Scientist to analyze large datasets and develop risk management strategies. You will collaborate with stakeholders to translate business problems into analytical solutions and present findings to drive responsible growth.

Responsibilities include building predictive models for credit risk and fraud analytics, creating dashboards, and ensuring data quality while working across data platforms and teams in a fast-paced environment.