Fraud Data Scientist — Hybrid ML & Fraud Analytics

UMB; Kansas City (MO); Hybrid; USD 98,000 - 145,000; Full time

Benefits offered by this job

Paid time off

401(k) matching

Annual incentive pay

Paid holidays

Medical, dental, vision insurance

Job summary

UMB in Kansas City, MO seeks a Fraud Data Scientist to advance analytics for fraud strategies and reporting consumed by executive leadership. You will work in a hybrid setting, collaborating with cross-functional teams to develop models and dashboards for fraud detection and investigation.

The role blends statistical modeling, ML, and data visualization with a focus on actionable insights and robust validation. A Master’s in a quantitative field and hands-on Python/R experience are required.

Qualifications

• Master’s degree in a quantitative field with 2+ years of unsupervised data modeling and segmentation experience or equivalent background.
• Strong foundation in math, statistics, and programming.
• Hands-on knowledge of R or Python, Snowflake, SQL Server and visualization tools like Tableau or Power BI.
• Experience in statistical algorithm development and evaluation.

Responsibilities

• Develop and implement statistical analysis, ML processes, and deep learning for fraud modeling.
• Apply dynamic techniques and ML to gain insights for fraud prevention.
• Answer what will happen and what should be done; gather business detail to refine models.
• Routinely measure model performance and implement modifications as needed.

Skills

Statistical modeling

Python

R

SQL

Machine learning

Data visualization

Critical thinking

Education

Master's degree in CS/Math/Statistics

Tools

Snowflake

SQL Server

Tableau

Power BI

Python

R

Job description

UMB in Kansas City, MO seeks a Fraud Data Scientist to advance analytics for fraud strategies and reporting consumed by executive leadership. You will work in a hybrid setting, collaborating with cross-functional teams to develop models and dashboards for fraud detection and investigation.

The role blends statistical modeling, ML, and data visualization with a focus on actionable insights and robust validation. A Master’s in a quantitative field and hands-on Python/R experience are required.