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Credit Risk Data Science Jobs in Munster, IN (NOW HIRING)

DV Trading

Credit Risk Manager

Chicago, IL Β· On-site

$150K - $200K/yr

DV is looking for a Credit Risk Manager to lead its counterparty and credit risk management ... Strong proficiency in Microsoft Office Suite, SQL, Python, data visualization tools * Excellent ...

DV Trading

Credit Risk Manager

Chicago, IL

DV is looking for a Credit Risk Manager to lead its counterparty and credit risk management ... Strong proficiency in Microsoft Office Suite, SQL, Python, data visualization tools * Excellent ...

DV Trading

Credit Risk Manager

Chicago, IL Β· On-site

$150K - $200K/yr

DV is looking for a Credit Risk Manager to lead its counterparty and credit risk management ... Strong proficiency in Microsoft Office Suite, SQL, Python, data visualization tools * Excellent ...

Fifth Third

Credit Risk Officer II

Chicago, IL

Using both internal and external data to identify portfolio trends such as increasing concentration ... Credit Risk Officer II Total Base Pay Range 121,900.00 - 262,100.00 USD Annual At Fifth Third, we ...

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Credit Risk Data Science information

See Munster, IN salary details

$36.1K

$111.1K

$192.7K

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

As of Sep 9, 2026, the average yearly pay for credit risk data science in Munster, IN is $111,136.00, according to ZipRecruiter salary data. Most workers in this role earn between $80,500.00 and $137,100.00 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.

What job categories do people searching Credit Risk Data Science jobs in Munster, IN look for?

The top searched job categories for Credit Risk Data Science jobs in Munster, IN are:

Credit Risk Data Science jobs near you
Infographic showing various Credit Risk Data Science job openings in Munster, IN as of August 2026, with employment types broken down into 1% As Needed, 82% Full Time, 13% Part Time, and 4% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $111,136 per year, or $53.4 per hour.

Sr Credit Risk Analyst - Credit Card Strategy

Chicago, IL

Tiger Analytics Inc.
Business Management Consulting • 201 - 500 employees
Learn more about Tiger Analytics Inc.external

Full-time

Re-posted 6 days ago

Job description

Tiger Analytics is an advanced analytics consulting firm. We are the trusted analytics partner for several Fortune 100 companies, enabling them to generate business value from data. Our consultants bring deep expertise in Data Science, Machine Learning and AI. Our business value and leadership has been recognized by various market research firms, including Forrester and Gartner.

We are looking for experienced Credit Risk Analyst to join its credit risk strategy team. This role will focus on credit card portfolio management, credit line strategies, and developing data-driven risk policies to optimize portfolio performance while effectively managing credit exposure. The ideal candidate will have strong hands-on experience with Credit Line Increase (CLI) strategies, credit card risk management, portfolio analytics, and credit policy development.

Responsibilities:

  • Develop, enhance, and manage credit card credit line and CLI strategies.
  • Analyze portfolio performance, customer behavior, credit risk, utilization, exposure, and profitability.
  • Develop customer eligibility criteria and line-assignment strategies based on risk and business objectives.
  • Analyze credit bureau, customer, and portfolio data to identify opportunities for improving credit strategies.
  • Design and evaluate strategy tests, experiments, and champion/challenger approaches.
  • Partner with Risk, Product, Analytics, and other stakeholders to implement new and enhanced credit policies.
  • Monitor the performance of existing credit strategies and recommend improvements based on portfolio trends.
  • Use SQL, SAS, Python, or similar tools to perform portfolio analysis and generate actionable insights.
  • Assess the impact of credit strategies on key metrics such as credit losses, utilization, exposure, revenue, and customer performance.
  • Translate complex analytical findings into clear recommendations for business and executive stakeholders.
  • Prepare presentations, analysis, and recommendations for senior management.
  • Ensure credit strategies align with risk appetite, business objectives, and applicable policies.
  • Support ongoing portfolio monitoring and identify emerging credit risk trends.

Requirements

  • 6 to 9 years of experience in credit card risk strategy, credit line management, or portfolio management.
  • Direct experience designing, developing, or materially enhancing Credit Line Increase (CLI) strategies and policies.
  • Strong understanding of: Credit card economics, Customer behavior, Credit bureau data, Affordability, Credit exposure management, Credit risk.
  • Experience developing and managing eligibility rules, decision waterfalls, and line-assignment strategies.
  • Experience designing, executing, or evaluating strategy tests and experiments.
  • Strong hands-on experience with SQL, SAS, Python, or similar analytical tools for portfolio analysis.
  • Ability to analyze large datasets and translate analytical findings into actionable business recommendations.
  • Strong stakeholder management and executive communication skills.
  • Experience working with cross-functional teams such as Risk, Product, Analytics, Compliance, and Operations.
  • Strong understanding of credit risk policies and the ability to translate analytical insights into implementable credit strategies and policies.

Benefits

Significant career development opportunities exist as the company grows. The position offers a unique opportunity to be part of a small, fast-growing, challenging and entrepreneurial environment, with a high degree of individual responsibility.

Tiger Analytics provides equal employment opportunities to applicants and employees without regard to race, color, religion, age, sex, sexual orientation, gender identity/expression, pregnancy, national origin, ancestry, marital status, protected veteran status, disability status, or any other basis as protected by federal, state, or local law.

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