Job Description

Our client is dedicated to empowering clients to achieve financial stability through competitive products, exceptional support, and expert guidance. They strive to be the leading choice for their target demographic.

Join a fulfilling career where our core principles – ethical conduct, trustworthiness, dedication, and service – define our interactions with colleagues and clients. Be part of a mission-driven environment where your contributions are highly valued.

The Opportunity

As a Senior Specialist in Quantitative Risk Analysis for Fraud, you will develop and implement advanced quantitative and analytical models, assessments, and applications to support risk management efforts, identifying market risks and process gaps. You will apply diverse methodologies and extensive experience in quantitative analytics to address complex and undefined risk challenges. You will collaborate with leadership to bridge gaps and improve alignment between existing practices and regulatory requirements. You will lead and execute complex initiatives and cross-functional teams, driving problem resolution. You will leverage broad organizational knowledge and business acumen related to core disciplines, products, and processes.

Additional Responsibilities Include:

• Design data mapping and graph schema for various identity fraud applications.
• Develop and maintain data extraction, transformation, and loading (ETL) scripts, data pipelines, and manage batch and incremental data ingestion into graph databases.
• Develop and optimize graph queries to identify diverse fraud patterns.
• Build self-service fraud intelligence tools for internal users.
• Conduct graph analytics, including graph traversal and implementing algorithms like community detection.
• Perform deep link analysis to identify interconnected fraudulent activities and conduct graph feature engineering to improve traditional fraud models.
• This position allows for remote work within the continental U.S., with occasional travel.

Key Responsibilities:

• Apply advanced analytical knowledge to assess future and emerging risks, opportunities, and efficiencies.
• Translate analytical results into actionable solutions for improved decision-making.
• Present findings to senior leadership.
• Apply expert knowledge and industry-standard methodologies to quantify risk and aggregate exposures.
• Develop advanced systems and programs to measure aggregate risk exposures.
• Perform model validations and present results to senior leadership.
• Utilize innovative and scientific/quantitative analytical approaches to draw conclusions and make recommendations that drive business objectives and change.
• Convert recommendations into effective communication materials for presentation to senior leadership.
• Provide subject matter expertise in operationalizing recommendations.
• Produce sophisticated analytical materials to lead discussions with cross-functional teams and senior leadership.
• Demonstrate thought leadership in cross-functional team environments.
• Lead and manage cross-functional teams in support of initiatives that significantly impact the organization.
• Provide guidance and mentorship to team members in the peer review process.
• Effectively communicate analytical results, findings, and solutions to governance committees and business process owners.

Required Skills:

• Bachelor’s degree in Economics, Finance, Statistics, Mathematics, Actuarial Sciences, or a related quantitative field (or 4 additional years of relevant experience).
• 8 years of related quantitative analysis experience in risk management, including statistical analysis, modeling, or mathematics.
• OR advanced degree in a quantitative field and 6 years of related work experience.
• OR PhD in a quantitative field and up to 4 years related work experience.

Preferred Skills:

• Experience with graph database technologies.
• Strong understanding of graph theory and algorithms.
• Proficiency in developing and optimizing graph queries.
• Experience with ETL processes and data pipeline development.
• Familiarity with fraud detection techniques and identity fraud use cases.
• Proficiency in programming languages such as Python, Java, or similar.
• Excellent problem-solving skills and attention to detail.
• Ability to work collaboratively in a team environment

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