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Credit Risk Data Science Jobs in Seattle, WA (NOW HIRING)

Grid

Applied Scientist

Seattle, WA

$120K - $200K/yr

... science. What you'll do * Research & Analysis: Perform data research and analysis using Grid ... controlling risk, etc. * Deployment & Iteration: Iterate on new and existing models based on ...

SoFi

Senior Product Manager, Transactional Risk

Seattle, WA · On-site

$144K - $190K/yr

Sitting at the intersection of Data Science, Engineering, and Payments, you will build high ... Card, Credit Card, Checks, P2P, International Remittances and Crypto. * Cross-Functional ...

Amazon

Scaled Abuse Specialist, Risk

Seattle, WA · On-site

... in risk, digital fraud, compliance who also have advanced data analysis skills (SQL, Python ... Data Science). This role will manage critical and high impact projects and scale their findings ...

Grid

Applied Scientist

Seattle, WA · On-site

$120K - $200K/yr

... science. What you'll do * Research & Analysis: Perform data research and analysis using Grid ... controlling risk, etc. * Deployment & Iteration: Iterate on new and existing models based on ...

GM Financial

ACF Sr Credit Analyst I

Lynnwood, WA · On-site

$61K - $116K/yr

Analyze credit data to determine the degree of risk involved in extending credit. * Make decisions to approve or deny the extension of credit within set credit authority established by senior ...

Salal Credit Union

Senior Portfolio Manager

Seattle, WA · On-site

$120K - $140K/yr

... to assess risk, structure sound credit decisions, and support sustainable growth. You'll ... Your work will help maintain data integrity, portfolio stability, and regulatory compliance while ...

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Showing results 1-20

All JobsCredit Risk Data Science JobsCredit Risk Data Science Jobs in Seattle, WA

Credit Risk Data Science information

See Seattle, WA salary details

$42.1K

$129.6K

$224.8K

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

As of Jun 9, 2026, the average yearly pay for credit risk data science in Seattle, WA is $129,600.00, according to ZipRecruiter salary data. Most workers in this role earn between $93,900.00 and $159,900.00 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.
What are popular job titles related to Credit Risk Data Science jobs in Seattle, WA? For Credit Risk Data Science jobs in Seattle, WA, the most frequently searched job titles are:
What job categories do people searching Credit Risk Data Science jobs in Seattle, WA look for? The top searched job categories for Credit Risk Data Science jobs in Seattle, WA are:
Credit Risk Data Science jobs near you
Infographic showing various Credit Risk Data Science job openings in Seattle, WA as of May 2026, with employment types broken down into 89% Full Time, 10% Part Time, and 1% Contract. Highlights an 74% Physical, 3% Hybrid, and 23% Remote job distribution, with an average salary of $129,600 per year, or $62.3 per hour.
Applied Scientist

Applied Scientist

Grid

Seattle, WA

$120K - $200K/yr

Full-time

Posted 20 days ago

Job description

About us
Today’s financial system is built to favor those with money. Grid’s mission is to level that playing field by building financial products that help users better manage their financial future. The Grid app lets users access cash, build credit, spend money, optimize their taxes, and lots, lots more.

Grid is a fast-growing team that’s deeply passionate about making a difference in the lives of millions. We’re solving huge problems and believe that every team member has a big role to play. Come join our growing team in our brand new Seattle office!

The role
We’re adding an Applied Scientist to our team to help us build and scale our core product lines. You'll work closely with product, engineering and business leaders to make a difference with data. With access to multiple robust datasets and clear research objectives, you'll have a significant impact on Grid's progress as a business—as well as our users' happiness and success.

Projects will include fraud detection, prevention and mitigation in novel arenas, such as risk underwriting for various lending/advance programs; predictive analytics to drive our payout and repayments systems; and more.
 
The team
We're focused on serving our users and building a robust product and business above all else. To this end, Grid's team members experience high levels autonomy and ownership, and as a company we value curiosity, learning and growth.

As an Applied Scientist, you'll have an opportunity not only to identify key leverage points for our data products, but also to set the standard for Grid's statistical inference and machine learning practice.

The tech stack
Our backend tech stack is based on Python, GCP, Go, protobufs, BigQuery and MySQL. We have built our platform from the ground up to optimize for clean data sources, and we have made numerous investments into data warehousing, streaming analytics infrastructure and offline data cleanliness. As a result, we think Grid is positioned for efficient and powerful applied science.
What you'll do
  • Research & Analysis: Perform data research and analysis using Grid's proprietary dataset as well as other relevant sources
  • Model Development: Develop and validate models that enable strategically relevant business objectives, such as enabling growth, mitigating fraud, controlling risk, etc.
  • Deployment & Iteration: Iterate on new and existing models based on feedback from team and real-world performance
  • Productionization: Collaborate with data engineers, product managers to help translate your work into production-grade, high scaled data products
  • Present Findings: Present your findings and communicate with members of the team with varying levels of technical depth
  • Foster DS @ Grid: Help build out our Applied Science and Machine Learning as a team and practice at Grid
What we're looking for:
  • Applied Science Expertise: Proven experience in Machine Learning and/or Applied Science, including a strong background in statistical inference, machine learning. This is a requirement, a bachelors or master's degree in Statistics, Mathematics, Physics, or Computer Science with a focus on machine learning is required. We are currently not accepting applicants with bachelor or master's degrees in Business Analytics, Information Systems, or Data Science.
  • Deep Expertise in Applied Science & Machine Learning: Proven experience in applied machine learning, including a deep understanding of statistical inference and predictive modeling. Demonstrated practical experience with deep learning techniques, particularly transformer-based models.
  • Research to Implementation Proficiency: A strong track record of reading, understanding, and implementing research papers in machine learning or related fields.
  • Robust Technical Skills: Hands-on experience with Python (with libraries like PyTorch/TensorFlow) and SQL is essential.
  • Autonomy and Initiative: Ability to work independently and take ownership of projects, showcasing a proactive approach to identifying key leverage points for data products.
  • Curiosity and Optimism: People who are constantly asking why the world around them works the way it does, and who have the will to change it.
  • Technical Skills: Proficiency in the modern machine learning techniques, such as Model Evaluation and Validation, Deep Learning and Time Series Analysis, Logistic Regression, Naive Bayes, Tree based Models (i.e., Random Forest).
  • Self Starter: Confidence to prioritize work and delivery demonstrable results on a tight cadence.
  • Domain Knowledge: Demonstrated experience or understanding of the financial industry, especially in the context of building and scaling FinTech products.
Benefits
Medical
Dental
Vision
401K
Life Insurance

Salary Range
$120,000 - $200,000 per year

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. 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.