FICO (NYSE: FICO) powers decisions that help people and businesses around the world prosper. Founded in 1956, the company is a pioneer in the use of predictive analytics and data science to improve operational decisions. FICO holds more than 200 US and foreign patents on technologies that increase profitability, customer satisfaction and growth for businesses in financial services, insurance, telecommunications, health care, retail and many other industries. Using FICO solutions, businesses in more than 100 countries do everything from protecting 4 billion payment cards from fraud, to improving financial inclusion, to increasing supply chain resiliency. The FICO® Score, used by 90% of top US lenders, is the standard measure of consumer credit risk in the US and has been made available in over 40 other countries, improving risk management, credit access and transparency.

The Opportunity:

As part of a team, you will participate in designing, developing, and deploying state-of-the-art, data-driven predictive models to solve business problems using the latest technologies in data mining, statistical modeling, pattern recognition, and performance inference. This is a unique opportunity to contribute to FICO Scores International, where you will learn and apply flagship FICO® Score expertise to global markets, collaborate with clients across regions, explore diverse data sources, work with large-scale datasets, and build models that drive real-world impact.

What You’ll Contribute:

Contribute to international FICO Scores projects through client data analysis, model development, model validation, and delivery activities.

Design, develop, and evaluate state-of-the-art, data-driven exploratory analyses, predictive models, and decision models to solve business problems across the financial services industry.

Analyze large-scale consumer credit and credit bureau datasets to assess modeling suitability, identify data patterns, develop variables, and support scoring solutions.

Apply statistical modeling, machine learning, pattern recognition, and performance inference techniques to identify effective modeling approaches and improve model performance.

Anticipate, investigate, and resolve data, modeling, and analytical issues by identifying root causes and recommending practical solutions.

Create, review, and deliver formal presentations of analytical work, clearly communicating modeling results, key insights, and recommendations to internal stakeholders and clients.

Manage assigned analytical work streams across simultaneous projects, adjusting to changing priorities while consistently delivering high-quality work against client and internal deadlines.

Communicate effectively with project teams and client stakeholders, including sharing progress, raising questions or risks early, and participating in meetings across international time zones.

Travel may be required occasionally for client meetings or project needs, including domestic or international engagements.

What We’re Seeking:

Master’s degree in Applied Mathematics, Economics, Statistics, Operations Research, Computer Science, Engineering, Physics, Cognitive Science, or a related quantitative, technical, or natural science discipline.

Relevant experience in financial services, fintech, insurance, consumer lending, or credit bureau environments; familiarity with consumer credit risk, fraud risk, credit bureau data, or lending decisioning preferred.

Strong programming and data analysis skills using languages or tools such as Python, R, C++, Java, MATLAB, or similar, with the ability to prepare, manipulate, analyze, and model large datasets.

Ability to analyze complex datasets, identify data, processing, or analytical issues, pinpoint root causes, and recommend practical solutions that support reliable modeling outcomes.

Self-starter with a curious and analytical mindset, able to take initiative, ask thoughtful questions, learn quickly, and follow through on high-quality deliverables.

Open and effective communicator, comfortable sharing progress, raising questions or risks early, and communicating effectively with stakeholders at different levels.

Ability to work both collaboratively and independently in cross-functional and client-facing environments.

Willingness to work on global projects, including occasional meetings outside normal business hours to accommodate international client time zones.

Exposure to cloud-based analytics platforms such as AWS, Azure, GCP, or similar is a plus.