The Enterprise Fraud Strategy group provides bank-wide support for detecting, preventing, and investigating fraudulent activity across multi-channel payment systems.

The Fraud Data Scientist reports directly to the Manager, Fraud Reporting & Analytics, supporting business reporting and analytical needs for fraud initiatives. The Fraud Data Scientist works as a member of a reporting and analytics team and utilizes tools for conducting analytics for managing fraud strategies, as well as creating reports consumed by LOB (lines of business) senior executives. This is a subset of the overall responsibilities which involves other multiple initiatives as assigned by Bank Operations leadership. 

This role is hybrid (Mon through Thu on-site / Fri remote) located in downtown Kansas City, MO.

How you will spend your time:

• Work closely across the organization to develop and implement statistical analysis, artificial intelligence, machine learning processes and algorithms, and deep learning for fraud modeling.
• Use dynamic techniques like Machine Learning to gain insights about the future.
• Answer the “What will happen, and What should be done?” questions.
• Gather business detail from peers and internal customers to adapt/enhance models.
• Routinely measure model performance and implement modifications as needed.

We are excited to talk if you have:

• Master’s Degree in Computer Science, Math, Economics, Statistics, or another quantitative field and 2+ years of unsupervised data modeling and segmentation experience OR any combination of education and experience that would provide an equivalent background.
• Proven expert level foundation of math, statistics, and programming.
• Demonstrated hands-on working knowledge of R or Python, Snowflake, SQL Server or similar database management tool and visualization tools such as Tableau or Power BI.
• Superior critical thinking skills.
• Strong proficiency in statistical algorithm development and evaluation.
• Proficiency in advanced statistics, hyperparameter tuning, A/B Testing, predictive modeling, recommendation systems, propensity scoring, natural language processing, clustering, customer segmentation, data acquisition, mining structured and unstructured data, designing tools, automation systems, and data frameworks.

Bonus Points if you have:

• Banking or financial services industry experience.
• Fraud reporting or analytics experience.

Applicants must have legal authority to work in the United States.  Work Visa sponsorship is not available for this position.

Compensation Range:

The posted compensation range on this listing represents UMB’s good faith and reasonable estimate based on its budget and what it expects to be the starting pay for this role, but the actual compensation may vary by geographic location, experience level, and other job-related factors. Please see the description of benefits included with this job posting for additional information.

UMB offers competitive and varied benefits to eligible associates, such as Paid Time Off; a 401(k) matching program; annual incentive pay; paid holidays; a comprehensive company sponsored benefit plan including medical, dental, vision, and other insurance coverage; health savings, flexible spending, and dependent care accounts; adoption assistance; an employee assistance program; fitness reimbursement; tuition reimbursement; an associate wellbeing program; an associate emergency fund; and various associate banking benefits. Benefit offerings and eligibility requirements vary.

Are you ready to be part of something more?
You're more than a means to an end—a way to help us meet the bottom line. UMB isn't comprised of workers, but of people who care about their work, one another, and their community. Expect more than the status quo. At UMB, you can expect more heart. You'll be valued for exactly who you are and encouraged to support causes you care about. Expect more trust. We want you to do the right thing, no matter what. And, expect more opportunities. UMBers are known for having multiple careers here and having their voices heard.

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