Risk Decision Model Development Intermediate Analyst

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Job Overview

We, at Citi, believe in the power of Data and the value Data Science can generate for our organization. Our high-quality financial products and services enable consumers and businesses to prosper and grow in domestic markets, as well as internationally. We put the customers at the center of everything we do and data at the center of how we do it. We continue to combine our scale, digital capabilities, and ecosystem to be where our customers need us to be. Data Science enables us to make our decisions better, faster and our products and services relevant to our customers

We have made it a priority to foster a culture of inclusion where the best people want to work, where people are promoted on their merits, where we value and demand respect for others, and where opportunities to develop are widely available to all.

This position is with US Consumer Cards (USCC) Risk Modeling Solutions (RMS). This specific role supports the US Consumer Bank’s Branded Cards portfolios. Citi-branded card products include its proprietary portfolio and co-branded cards.

In this role, you will play a critical part in developing advanced Risk Decision Models that power strategic decision‑making across the organization. You will work with large and complex datasets – including traditional and alternate data sources – to build high‑performing analytical solutions. A key objective of this role is to drive the adoption of AI across risk decisioning, where proficiency in Generative AI (GenAI), Large Language Models (LLMs), and Agentic Architectures will be a significant advantage. You will apply cutting‑edge Machine Learning and statistical techniques and collaborate across functions to deliver models that are robust, compliant, and aligned with evolving business needs.

Responsibilities:

Model Development & Analytics:

• Build Risk Decision Models using Machine Learning, advanced statistical methods, and numerical algorithms.

• Develop, validate, and enhance models that support risk strategies, ensuring full alignment with:

  • Risk policies and modeling procedures

  • Model Risk Management (MRM) guidelines

  • Fair Lending, model interpretability, and other regulatory expectations

Data Preparation & Feature Engineering:

• Leverage tools such as Python, SAS, PySpark, and other analytical platforms to extract, clean, and transform data.

• Engage with both traditional and alternate data sources, performing data preparation, feature engineering, and variable selection for model development.

Model Lifecycle Management

• Own the complete model development lifecycle including:

  • Problem definition & model design

  • Data preparation

  • Model training, testing, and tuning

  • Out‑of‑sample and time‑based validation

  • Comprehensive documentation

  • Stakeholder presentations and model governance interactions

  • Implementation support with Technology teams

Collaboration & Stakeholder Engagement

• Partner with Technology, Risk Policy, Governance, and Product teams to ensure seamless execution and timely delivery.

• Communicate complex analytical concepts clearly to both technical and non‑technical audiences through compelling storytelling and presentations.

Qualifications:

• 4+ years of hands‑on experience in Risk Modeling.

• Strong foundation in statistical modeling, econometrics, Machine Learning, numerical methods, and industry best practices for model development and validation.

• Proven experience developing or supporting risk models, with the ability to identify patterns, trends, and insights from complex datasets.

• Proficiency in analytical and data manipulation tools such as Python, SAS, SQL, R, and Spark; experience working in Big Data environments is highly desirable.

• Strong working knowledge of the MS Office suite, especially Excel and PowerPoint, for analysis, reporting, and stakeholder communication.

• Excellent written and verbal communication skills with the ability to clearly articulate complex quantitative work to technical and non‑technical audiences.

• Highly self‑motivated, detail‑oriented, and able to work independently while collaborating effectively with cross‑functional teams.

• Demonstrated intellectual curiosity and commitment to continuous learning, particularly in staying abreast of new modeling techniques, tools, and technological advancements.

• Additional hands‑on expertise in Generative AI, Large Language Models (LLMs), or Agentic Architectures is a strong plus.

Education:

• Bachelor’s/ University degree in quantitative discipline (STEM: Science, Technology, Engineering, Mathematics or Statistics, Economics, Data Science).  Master’s/PhD degree is a plus.

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Job Family Group:

Risk Management

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Job Family:

Model Development and Analytics

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Time Type:

Full time

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Most Relevant Skills

Analytical Thinking, Business Acumen, Constructive Debate, Data Analysis, Escalation Management, Policy and Procedure, Policy and Regulation, Risk Controls and Monitors, Risk Identification and Assessment, Statistics.

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Other Relevant Skills

For complementary skills, please see above and/or contact the recruiter.

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