Data Analytics Consultant (Credit Specialist)

MeridianLink is seeking a Data Analytics Consultant with a strong foundation in credit risk analysis and consumer lending performance to support financial institutions in optimizing their underwriting and decisioning strategies. This role focuses on analyzing credit data, loan performance, and bureau information to help clients improve approval rates, manage risk, and enhance portfolio health. In this role, you will partner closely with clients and internal teams to evaluate credit policies, interpret credit reports and performance trends, and translate analytical findings into practical lending recommendations. You will lead multiple client engagements, guiding institutions through data-driven insights that support responsible growth and sound risk management. As a trusted advisor within the Analytics Team, you will help clients understand how MeridianLink’s decisioning platforms and analytics tools can be used to strengthen underwriting, improve member experience, and drive sustainable lending outcomes. This role is ideal for individuals who are passionate about consumer credit, enjoy working directly with financial institutions, and take pride in delivering clear, actionable insights that influence lending strategy.

Key Responsibilities

• Develop a strong understanding of MeridianLink decisioning platforms, including MeridianLink Consumer and DecisionLender, and how applications flow through the underwriting and approval process.
• Analyze credit bureau data, application data, and loan performance data to evaluate portfolio risk, approval trends, and delinquency patterns.
• Review and assess underwriting guidelines, scorecards, and decision rules to identify opportunities for optimization.
• Interpret consumer credit reports, including tradelines, inquiries, utilization, and public records, to support risk profiling and policy recommendations.
• Evaluate key lending metrics such as approval rate, decline rate, capture rate, delinquency, charge-offs, and loss rates.
• Support clients in balancing risk management and growth through data-backed policy and strategy recommendations.
• Prepare client-facing reports and dashboards that clearly communicate portfolio performance and risk trends.
• Lead and participate in client meetings to present findings, explain credit risk drivers, and discuss optimization strategies.
• Collaborate with internal analytics, engineering, and product teams to deliver high-quality analytics solutions.
• Ensure accuracy and consistency across all analytical deliverables.
• Contribute to internal best practices, documentation, and training related to credit risk and lending analytics.
• Assist in developing scalable tools and templates for portfolio monitoring and underwriting analysis.
• Serve as a trusted advisor to clients on industry best practices in credit risk management and consumer lending.

Qualifications

• Bachelor’s degree in Finance, Economics, Business, Statistics, Mathematics, Data Analytics, or a related field preferred.
• 3–6 years of experience in credit risk analysis, underwriting, lending analytics, or financial services consulting preferred.
• Experience working with consumer credit data, bureau reports (Experian, Equifax, TransUnion), or loan performance data strongly preferred.
• Solid understanding of consumer lending fundamentals, including underwriting, risk tiers, scorecards, and policy rules.
• Experience in analyzing loan performance, delinquency, and loss trends.
• Strong analytical and problem-solving skills with the ability to translate data into business recommendations.
• Proficiency in Microsoft Excel and PowerPoint for analysis and client presentations.
• Experience with business intelligence tools (Power BI, Sisense, or similar)
• Experience with SQL, Python, or other analytical tools.
• Strong project management and organizational skills, with the ability to manage multiple client engagements.
• Excellent written and verbal communication skills, with the ability to explain credit risk concepts to technical and non-technical audiences.
• High attention to detail and strong commitment to data quality and accuracy.

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