Senior Data Scientist, Fraud & Risk Analytics

Shamrock Trading Corp.; Overland Park (KS); On-site; USD 110,000 - 150,000; Full time

Job summary

Shamrock Trading Corporation is seeking a Data Scientist to advance fraud detection and risk assessment across factoring, payments, lending, and freight marketplace operations. You will work with stakeholders and ML engineers to explore data, build models, and translate findings into actionable risk decisions.

The role emphasizes statistical methods, model evaluation, and collaboration with Product, Credit, Risk, and Operations to reduce losses and improve customer funding decisions.

Qualifications

• Degree in a quantitative field or equivalent practical experience.
• Experience with SQL and relational databases.
• Familiarity with cloud platforms (Databricks, AWS).
• Experience communicating analysis to stakeholders.

Responsibilities

• Translate fraud, risk, and credit questions into analytical approaches.
• Analyze invoices, funding, payments, and related data to identify risk patterns.
• Develop and evaluate ML models for fraud detection and risk scoring.
• Collaborate with Product, Credit, Risk, and Operations to define signals and metrics.
• Coordinate with ML team to productionize models and integrations.
• Link fraud initiatives to reporting and performance monitoring.
• Document assumptions, data limits, and model performance.
• Stay current on fraud patterns across lending, payments, and logistics.

Skills

Statistical thinking

Analytical problem solving

Data storytelling

Curiosity

Education

Bachelor’s degree in Statistics/Data Science/Analytics or related field

Tools

Databricks

AWS

SQL

Git

Power BI

Tableau

Job description

Shamrock Trading Corporation is seeking a Data Scientist to advance fraud detection and risk assessment across factoring, payments, lending, and freight marketplace operations. You will work with stakeholders and ML engineers to explore data, build models, and translate findings into actionable risk decisions.

The role emphasizes statistical methods, model evaluation, and collaboration with Product, Credit, Risk, and Operations to reduce losses and improve customer funding decisions.