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Credit Risk Data Science Jobs (NOW HIRING)

Credit Risk Analyst

$102K - $140K/yr

A Credit Risk Analyst at Prosper has the opportunity to utilize advanced analytical skills to ... Partner with Data Science, Product, Engineering and other partner teams to implement credit and ...

Credit Risk Analyst

Charleston, WV · Remote

$102K - $140K/yr

A Credit Risk Analyst at Prosper has the opportunity to utilize advanced analytical skills to ... Partner with Data Science, Product, Engineering and other partner teams to implement credit and ...

Director of Credit Risk

Manhattan, NY · On-site

$160K - $190K/yr

Leadership Role In Risk And Data Science At Biz2Credit, we look for individuals who are ready to ... Responsibilities Credit Decisioning Model Development * Develop and implement advanced credit risk ...

Credit Risk Associate

New York, NY · On-site

$108K - $200K/yr

Credit Risk Strategy owns these tradeoffs end to end. In this role, you will own or help build ... Engineering, Data Science, Risk Operations, Finance, Customer Experience, and Compliance. AI ...

Showing results 41-60

Credit Risk Data Science information

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$37K

$113.9K

$197.5K

How much do credit risk data science jobs pay per year?

As of Aug 22, 2026, the average yearly pay for credit risk data science in the United States is $113,881.00, according to ZipRecruiter salary data. Most workers in this role earn between $82,500.00 and $140,500.00 per year, depending on experience, location, and employer.

What is credit risk data science?

Credit Risk Data Science is a specialized field that uses statistical analysis, machine learning, and data modeling techniques to assess and predict the likelihood that a borrower will default on a loan or credit obligation. Professionals in this field analyze large datasets from financial transactions, credit reports, and market trends to develop models that help financial institutions make informed lending decisions. Their work helps manage risk, set appropriate interest rates, and comply with regulatory standards. By leveraging advanced analytics, credit risk data scientists play a crucial role in minimizing losses and maximizing profitability for banks and lenders.

What skills and qualifications are needed to thrive as a credit risk data scientist?

To thrive as a Credit Risk Data Scientist, you need strong analytical skills, proficiency in statistical modeling, and a solid background in finance, mathematics, or a related field, often supported by an advanced degree. Familiarity with programming languages like Python or R, experience with machine learning frameworks, and knowledge of credit risk modeling tools such as SAS or SQL are typically required. Critical thinking, attention to detail, and effective communication are vital soft skills for interpreting data and collaborating with stakeholders. These abilities are crucial for building accurate risk models, informing strategic decisions, and ensuring regulatory compliance in financial institutions.

How does a credit risk data scientist typically collaborate with other teams within a financial institution?

Credit Risk Data Scientists often work closely with credit analysts, risk managers, and IT professionals to develop, validate, and implement models that assess borrower risk. They frequently participate in cross-functional meetings to translate complex analytical findings into actionable business insights. Collaboration with compliance and regulatory teams is also common to ensure that risk models meet current regulatory standards. Effective communication and teamwork are essential, as the role bridges technical model development and practical risk management decisions.
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What cities are hiring for Credit Risk Data Science jobs?

Cities with the most Credit Risk Data Science job openings:

What states have the most Credit Risk Data Science jobs?

States with the most job openings for Credit Risk Data Science jobs include:

Infographic showing various Credit Risk Data Science job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 85% Full Time, 11% Part Time, and 3% Contract. Highlights an 85% Physical, 4% Hybrid, and 11% Remote job distribution, with an average salary of $113,881 per year, or $54.8 per hour.

Senior Enterprise and Credit Risk Analyst

Federal Home Loan Bank of New York

Manhattan, NY • On-site

$135 - $155/hr

Other

Posted 9 days ago


Job description

Enterprise Risk Analytics (ERA) resides in the Risk Management Group. RMG serves as an independent point for all risk related issues that affect the Bank’s strategic and business plans and its operating and financial performance. The ERA group is responsible for the Credit Risk Model ownership and credit risk analytics capital allocation, risk exposure measurement methodology, calculation, analysis and reporting and definition of appropriate policy standards/limits, procedures, and metrics.

Position Summary

The Enterprise and Credit Risk Data Analyst supports the Bank's risk management framework by developing and enhancing risk reporting, analytics, and data management solutions. This role is responsible for designing automated reports and dashboards, analyzing risk data from multiple sources, and delivering timely insights that support management and regulatory reporting. The analyst partners with business and technology teams to improve data quality, streamline reporting processes, maintain documentation, and implement effective controls. The ideal candidate combines strong analytical, technical, and problem-solving skills with a commitment to continuous improvement and industry best practices in risk reporting and data governance.

Essential Job Functions
  • Support Risk Reporting and Analytics by developing, maintaining, and delivering recurring and ad hoc reports, dashboards, and analyses used to monitor and communicate enterprise and credit risk for management and regulatory purposes.
  • Develop and Enhance Reporting Solutions by building and maintaining automated reporting processes, databases, dashboards, and visualization tools (e.g., Qlik, WEBI, Microsoft PowerPoint) that improve efficiency, data accessibility, and risk insights.
  • Manage Risk Data and Data Quality by integrating data from multiple sources, ensuring data accuracy and completeness, identifying reporting gaps, resolving data issues, and implementing process improvements and quality controls.
  • Collaborate Across the Organization with business partners, technology teams, and vendors to support reporting needs, streamline processes, and enhance risk data and analytics capabilities.
  • Maintain Documentation and User Support by developing and maintaining data dictionaries, policies, procedures, process documentation, and user guides, while providing training and support for reporting and analytics tools.
  • Drive Continuous Improvement by monitoring industry trends, regulatory developments, and best practices to enhance the Bank's risk reporting, analytics, and data governance framework.
QualificationsGeneral & Communication Qualifications
  • Strong understanding of enterprise, credit, and market risk management, including risk measurement, financial statement analysis, risk reporting, governance, and regulatory requirements.
  • Demonstrated analytical, problem-solving, and decision-making skills with the ability to assess risks, identify trends, and recommend effective solutions.
  • Self-motivated professional with the ability to work independently, manage multiple priorities, and exercise sound judgment in a fast-paced environment.
  • Experience driving process improvements, automation, and operational efficiencies through scalable risk management and reporting solutions.
  • Strong project management and organizational skills with the ability to coordinate across multiple stakeholders and manage competing deadlines.
  • Excellent written, verbal, presentation, and interpersonal communication skills, with the ability to convey complex analytical and risk-related concepts to diverse audiences, including senior management and regulators.
  • Collaborative team player with the flexibility to adapt to changing priorities and support cross-functional initiatives.
Technical Qualifications
  • Advanced coding skills in Python and R.
  • Strong experience working with databases, SQL, and large data sets.
  • Ability to collect, analyze, and validate data from multiple sources to support risk reporting and decision-making.
  • Experience developing reports, dashboards, and visualizations using tools such as Power BI, Qlik Sense, Tableau, or similar platforms.
  • Advanced proficiency in Microsoft Excel, including data analysis, reporting, and process automation.
  • Knowledge of data governance, data quality, and data management best practices.
  • Ability to document and maintain data processes, reports, and procedures to support operational effectiveness and knowledge sharing.
  • Experience evaluating and improving reporting tools, business applications, and technology solutions.
  • Experience: 5+ years of progressively responsible experience in the financial services industry with exposure in risk management, finance accounting and data and process management.
  • Understanding of credit metrics, relational databases, and visualization tools.
  • Knowledge of capital markets transactions, and/or balance sheet analytics preferred.
  • Education: Bachelor’s degree in finance, Economics, Business, Computer Science or a related field, an advanced degree or equivalent work experience is preferred.

Base Salary Range: $135,000-$155,000

The Federal Home Loan Bank of New York is committed to recruit, hire, develop, motivate, promote, retain, and compensate all applicants and employees in a nondiscriminatory manner without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, status as a parent, disability, genetic information, military or veteran status, or any other characteristic protected by applicable law (including title VII of the Civil Rights Act of 1964).

The Federal Home Loan Bank of New York is committed to recruit, hire, develop, motivate, promote, retain, and compensate all applicants and employees in a nondiscriminatory manner without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, status as a parent, disability, genetic information, military or veteran status, or any other characteristic protected by applicable law (including title VII of the Civil Rights Act of 1964).

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