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Credit Risk Data Science Jobs in Grand Prairie, TX

IntelliPro Group Inc.

Credit Risk Analyst

Plano, TX · On-site

$37 - $51/hr

We are looking for a detail-oriented and data-driven Credit Risk Analyst to join our Credit Risk ... Bachelor's degree or higher in Analytics, Statistics, Finance, Accounting, Math, Computer Science ...

IntelliPro Group Inc.

Credit Risk Analyst

Plano, TX · On-site

We are looking for a detail-oriented and data-driven Credit Risk Analyst to join our Credit Risk ... Bachelor's degree or higher in Analytics, Statistics, Finance, Accounting, Math, Computer Science ...

Regional Finance

Director, Credit Risk

Plano, TX · On-site

About the Role The Director, Credit Risk Management, leads the development and execution of credit ... The Director drives data-driven decision-making to optimize portfolio performance, balancing growth ...

Regional Finance

Director, Credit Risk

Plano, TX

About the Role The Director, Credit Risk Management, leads the development and execution of credit ... The Director drives data-driven decision-making to optimize portfolio performance, balancing growth ...

Regional Finance

Manager Credit Risk

Plano, TX · On-site

This positions will utilize advance skills to analyze data, portfolio level performance trends, custom scorecard analysis, and forecasting skills to support credit risk functions. Duties and ...

Lendistry

Sr. Data Scientist

Dallas, TX · On-site

The Senior Data Scientist works with Credit, Risk, and Engineering teams to build and maintain ... of hands-on experience indata science, credit risk analytics, or quantitative modeling.

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Credit Risk Data Science information

See Grand Prairie, TX salary details

$35K

$107.8K

$186.9K

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

As of Jul 14, 2026, the average yearly pay for credit risk data science in Grand Prairie, TX is $107,794.00, according to ZipRecruiter salary data. Most workers in this role earn between $78,100.00 and $133,000.00 per year, depending on experience, location, and employer.

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 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 are the key skills and qualifications needed to thrive as a Credit Risk Data Scientist, and why are they important?

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.
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Credit Risk Data Science jobs near you
Senior Data Scientist (Credit Risk)

Senior Data Scientist (Credit Risk)

Braviant Holdings

Dallas, TX • On-site, Remote

Full-time

Medical, Dental, Vision, Retirement, PTO

Re-posted 4 days ago

Job description

Title: Senior Data Scientist
Function: Credit Risk
Reports to: Head of Credit
Level: Mid-Level / Senior
Location: Addison, TX (5 days/week in-office)

Please note: This position is open to candidates within commuting distance to the DFW metro area only. Applicants must reside in Texas and be authorized to work in the United States. Applications from candidates outside of Texas will not be considered at this time. While we appreciate interest from all applicants, Braviant Holdings is unable to sponsor visas at this time.

Who We Are

Founded in 2015 and based in Chicago, IL, privately held Braviant Holdings, LLC is a leading provider of tech-enabled consumer credit products that combine breakthrough technology and cutting-edge machine learning to transform how people access credit online. Our next-generation approach to lending reduces credit barriers and creates a Path to Prime, helping millions of underbanked consumers build credit history, reduce their cost of borrowing, and take control of their personal finances. Braviant has been named multiple times to the Inc. 5000 list of fastest growing private companies and has been recognized as a Best Place to Work.

We are a lean team of approximately 40 people. Everyone rolls up their sleeves here, including this role.

About the Role
We are building and scaling a high-performance consumer lending platform and are looking for a Senior Data Scientist to drive credit decisioning across the loan lifecycle. This role sits at the intersection of credit strategy, fraud, and analytics, directly impacting approval strategy, loss performance, and portfolio profitability. You will build and deploy models that inform key decisions around who we approve, how we price risk, and how we manage portfolio performance. This is a hands-on, high-impact role suited for someone who is business-oriented, data-driven, and biased toward action, not just model development. You will partner closely with Credit, Fraud, Servicing, Product, and Engineering to translate data into clear, actionable decisions that improve approval quality, reduce early loss, and drive sustainable growth.
What You'll Be Doing
  • Develop and deploy predictive models across the credit lifecycle (acquisition, risk, and collections) with a focus on improving approval quality and loss performance.
  • Translate model outputs and analysis into actionable credit strategy, including approval cutoffs, segmentation, and decision rules.
  • Analyze portfolio performance (FPD, delinquency, loss) to identify key drivers of deterioration and recommend targeted actions.
  • Evaluate tradeoffs between approval rate, loss, and profitability, and recommend strategies to optimize portfolio performance.
  • Distinguish fraud risk vs credit risk, improving early default performance and reducing losses.
  • Design and execute experiments (A/B tests, champion/challenger frameworks) to evaluate strategies and drive continuous improvement.
  • Work with Product and Engineering to implement decisioning logic into production systems and ensure accurate execution.
  • Monitor model and strategy performance over time, identifying drift, instability, or unintended impacts on portfolio outcomes.
  • Collaborate cross-functionally with other departments to ensure decisions align with business goals and risk appetite.
What You Will Bring

Required

  • Degree in Data Science, Applied Mathematics, Statistics, Economics, Computer Science or a related field
  • 5-7 years of professional experience in Data Science, Analytics or a related field within FinTech or online lending space.
  • Advanced proficiency in Python for programming, data analysis, and predictive modeling
  • Proficiency in SQL, Excel and experience with data visualization tools
  • Excellent knowledge in applied statistical methods and experience using various predictive machine learning techniques including: linear models, decision trees, boosting, and ensemble models
  • Knowledge of optimization, stochastic processes, experimental design, A/B testing and bootstrapping
  • Passion for keeping your skills up to date and exploring new methodologies
  • The ability to distill complex problems and analysis into a clear and concise narrative
 
Preferred
  • Experience in subprime consumer lending, fintech, payments, or another regulated financial services technology environment.
  • Hands-on experience applying AI to credit risk management
Benefits & Perks

Compensation at Braviant is competitive and commensurate with experience. Details will be discussed with qualified candidates during the interview process. In addition, we provide:

  • Comprehensive healthcare including medical, dental, and vision coverage
  • Generous paid time off, including PTO, sick time, and 13 company holidays
  • 401(k) with company contribution
  • Participation in annual discretionary bonus plan
  • Regular team and company gatherings
Braviant is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We do not discriminate on the basis of race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity or expression, age, marital status, veteran status, disability status, or any other characteristic protected by applicable law.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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