Senior Data Scientist

About the Role

Shamrock Trading Corporation is looking for a Data Scientist to join our team with a focus on fraud detection and risk assessment across our factoring, payments, lending, and freight marketplace businesses. Data Scientists partner closely with business stakeholders, analysts, and Machine Learning Engineers to explore data, build and evaluate models, and communicate findings that drive efficiency, enable risk management, and support strategic objectives.

This role will apply statistical, analytical, and machine learning techniques to help Shamrock better identify suspicious behavior, reduce financial loss, improve risk decisioning, and support faster, more confident customer funding decisions. The Data Scientist will work closely with Product, Credit, Risk, Operations, Data Services, Machine Learning Engineers, and business stakeholders to understand fraud patterns, build risk signals, evaluate models, and translate findings into practical business action.

This role is well-suited for a data scientist or advanced analytics professional with experience in fraud, risk, credit, marketplace abuse, payments, lending, logistics, or identity verification who wants to apply those skills to complex real-world problems in freight and financial services.

What You’ll Do

Translate fraud, risk, and credit questions into analytical and modeling approaches
Analyze invoice, funding, payment, carrier, debtor, customer, behavioral, and operational data to identify risk patterns and emerging fraud trends
Develop and evaluate analytical and machine learning models to support fraud detection, risk scoring, anomaly detection, and early warning indicators
Partner with Product, Credit, Risk, Audit, Operations, and business stakeholders to define risk signals, success metrics, decision thresholds, and model evaluation criteria
Coordinate with Machine Learning team to transfer custom models into production deployments and platform integrations
Partner with Data Analysts and BI teams to connect fraud and risk initiatives with reporting, monitoring, and business performance metrics
Document assumptions, methodologies, data limitations, model performance, and findings to support transparent decision-making
Stay current on fraud patterns and risk approaches across factoring, freight, logistics, payments, lending, and marketplace businesses

What You’ll Bring

Required

Bachelor’s degree in a quantitative field (Statistics, Data Science, Analytics, Computer Science, Applied Mathematics, Engineering), or equivalent practical experience
Experience with relational databases and SQL
Experience using statistical programming languages (Python, R, etc.)
Knowledge of modern data platforms or cloud environments (Databricks, AWS, etc.)
Experience using Git and collaborative development workflows
Experience applying statistical or analytical techniques to real-world business problems (regression, classification, distributions, optimization, etc.)
Experience visualizing/presenting data for stakeholders (Power BI, Tableau, etc.)
Strong analytical thinking, problem-solving skills, and curiosity about business, fraud, and risk drivers
A drive to learn new technologies, fraud patterns, risk techniques, and industry context

Preferred

Experience in fraud analytics, risk analytics, credit risk, underwriting, payments fraud, marketplace fraud, identity verification, logistics, freight brokerage, factoring, asset-based lending, SMB lending, commercial lending, or financial services
Experience building or supporting fraud detection, anomaly detection, risk scoring, credit scoring, identity risk, or transaction monitoring models
Experience working with adversarial risk problems where bad actors change behavior in response to controls
Experience with entity resolution, graph analytics, network analysis, behavioral analytics, or relationship-based risk detection
Experience working with invoice, payment, bank account, carrier, debtor, customer, document, device, or transaction-level data
Experience partnering with Product, Engineering, Operations, Credit, Risk, or Compliance teams to move analytical work into business processes or production systems