Senior Data Scientist
Indexed description
Shamrock’s mission since the start has been to create value and success for our customers, our partners and our people. Strong ethics, dedication to our customers and close attention to the marketplace are critical to the success and growth of the Shamrock brands. Shamrock is headquartered in Overland Park, Kansas, with multiple offices throughout the U.S. Overland Park is a great place to live, work and play, being conveniently located within driving distance of everything Kansas City has to offer. Housed within the heart of Overland Park, our offices include 4 gorgeous towers on the East and West sides of Metcalf Ave. With a heavy community presence and a winning culture, Shamrock is a great place to work in Overland Park!
Why You’ll Love Working Here
Our award-winning culture is a testament to our commitment to growth, recognition and empowerment. From day one, we prioritize employees first, encouraging them to think and act like owners and providing endless opportunities for growth and self-development, both personally and professionally. Shamrock has been recognized as one of America’s 2025 “Most Loved Workplaces” by Newsweek.
Benefits
At Shamrock, we hire bright, ambitious people and give them the tools they need for a successful, long-term career here. Shamrock also offers a premier set of benefits for employees and their families:
- Training and Development: Ongoing training and professional development opportunities
- Medical: Fully paid healthcare, dental and vision premiums for employees and eligible dependents, and gym benefits
- Financial: Generous company 401(k) contributions and employee stock ownership after one year
- Work-Life Balance: Competitive PTO and fully paid time off opportunities to volunteer within your community
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
- 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
- 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
Having trouble submitting your application? Contact us at [email protected] for support.
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