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Credit Risk Data Science Jobs in Missouri (NOW HIRING)

Inland Empire Health Plan

Data Scientist II

California, MO · On-site

$118.60 - $157.14/hr

Collaborating closely with Data Science Developers, this role partners with our business ... Build deep-dive analyses and eventual machine learning models to better forecast risk across the ...

TAMKO

Manufacturing Data Scientist

Joplin, MO

... risk. * Interpret an extensive variety of technical instructions in mathematical or diagram form ... Bachelor's degree in Mathematics, Science, Engineering, Computer Science, Data Science, Statistics ...

savealot

IT Manager - Data Engineering

Saint Ann, MO · On-site

Additionally, these teams provide expertise in reporting, business intelligence, data science, data ... Partner with the security and risk teams to ensure secure, compliant analytics and reporting ...

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Credit Risk Data Science information

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 are popular job titles related to Credit Risk Data Science jobs in Missouri?

For Credit Risk Data Science jobs in Missouri, the most frequently searched job titles are:

What job categories do people searching Credit Risk Data Science jobs in Missouri look for?

The top searched job categories for Credit Risk Data Science jobs in Missouri are:

What cities in Missouri are hiring for Credit Risk Data Science jobs?

Cities in Missouri with the most Credit Risk Data Science job openings:

Credit Risk Data Science jobs near you

Senior Data Scientist - AI Specialist

Jobtailor

Dearborn, MO • On-site

$140 - $190/hr

Other

Posted 18 days ago

Job description

Responsibilities
  • As a Senior Data Scientist, you will use your knowledge of data and advanced analytics to identify and articulate the role data and analytics products play in helping the business achieve their goals.
  • You will collaborate with Data Engineers and Software Engineers to develop robust analytics products.
  • You will collaborate with partners in purchasing, product development, manufacturing, warranty, material planning, logistics and other Ford functions to define problems, identify data, develop data pipelines, develop metrics, develop analytics products using your expertise in visualization, AI/ML, Statistics and Optimization, create machine learning models, leverage operations research techniques, and deploy software solutions to provide actionable insights that deliver measurable via Google Cloud Platform to optimize the delivery of value.
  • Designing, training, and fine‑tuning AI models (including deep learning and LLMs) to solve specific business problems.
  • Familiar with LLM orchestration workflows like Langraph and Google ADK for quick development and scaling strategies to build robust pipelines.
  • Familiar with AI Graph DB platform, use native graph query and conduct LLM extraction into actionable insights.
  • Transitioning models from research environments to production, often by converting them into APIs or integrating them into existing software applications.
  • Building and maintaining the infrastructure for AI development, data pipelines, and automated workflows.
  • Working with data scientists to define AI strategies, understand requirements, and implement solutions.
  • Testing, validating, and monitoring AI models in production scale to ensure reliability.
  • Staying current with AI advancements (e.g., generative AI, LLMs) and applying them to improve existing products.
  • Accelerate the application of value‑added analytics and machine learning into the portfolio of products for the supplier risk team.
  • Drive analytic excellence into product teams by collaborating with Data Scientists, Data Engineers and Software Engineers in analytic and machine‑learning methods.
  • Work closely with the Product Manager and Product Owner to translate Business Value needs into analytic deliverables and, where appropriate, software products for delivery by product teams.
  • Act as a consultant to the business vs. an order taker.
Requirements
  • Master’s degree in quantitative fields, such as Data Science, Engineering, Operations Research, Industrial Engineering, Statistics, Mathematics, or Computer Science or equivalent combination of relevant education and experience.
  • 5+ years of hands‑on experience with Python, SQL, mathematical programming, machine learning, artificial intelligence, optimization/simulation techniques, or statistical analysis, capable of using at least three of the following visualization/dashboard tools: Angular, React, Tableau, Looker, PowerBI.
  • 3+ years of experience delivering analytics solutions.
  • 3+ years of experience with Agile team methodology.
  • PhD degree is preferred in quantitative fields, such as Data Science, Engineering, Operations Research, Industrial Engineering, Statistics, Mathematics, Computer Science, or related field.
  • Proven experience with developing data products/solutions to support analytic applications in Ford’s data ecosystem.
  • Experience with Neo4j development or related graph DB.
  • Experience with Product‑Driven Operating Model or Agile Product Development Process.
  • Proven proficiency in developing and deploying analytic models, working in a team environment, supporting customers and/or end users.
  • Comfortable working in an environment where problems are not always well‑defined.
  • Strong interpersonal and leadership skills, with ability to communicate complex topics to leaders and peers in a simple and clear manner.
  • Well‑organized, independent, and ready to work with minimal supervision.
  • Inquisitive, proactive, and interested in learning new tools and techniques.
  • Demonstrated hands‑on experience with deploying data products and/or analytic models in Ford’s on‑prem and/or Google Cloud Platform.
  • Demonstrated experience translating real‑world business problems into analytical formulations and interpreting analytics results with non‑analytics business partners.
  • Work experience in automotive industry is a big plus, as is experience in procurement, logistics, or program management function.
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