Every day, Global Payments makes it possible for millions of people to move money between buyers and sellers using our payments solutions for credit, debit, prepaid and merchant services. Our worldwide team helps over 3 million companies, more than 1,300 financial institutions and over 600 million cardholders grow with confidence and achieve amazing results. We are driven by our passion for success and we are proud to deliver best-in-class payment technology and software solutions. Join our dynamic team and make your mark on the payments technology landscape of tomorrow.
Global Payments is the parent company for TSYS, Netspend, Heartland, TouchNet, OpenEdge, Xenial, Greater Giving, ACTIVE Network, and AdvancedMD. Headquartered in Georgia with over 24,000 employees worldwide, Global Payments is a member of the S&P 500 with worldwide reach spanning over 100 countries throughout North America, Europe, Asia Pacific and Latin America. For more information, visit globalpaymentsinc.com and follow Global Payments on Twitter LinkedIn and Facebook.
The Senior Data Science Specialist for Risk & Fraud provides essential visibility into the conditions, attributes and behaviors that drive fraud loss. The Senior Data Science Specialist also provides innovative solutions to optimize fraud operations to prevent and mitigate additional fraud loss.
The Senior Data Science Specialist fuels data driven decisions by providing advanced analytics and predictive modeling to discover, monitor and forecast high risk card holder behavior that leads to dispute claims, direct payouts, transaction alerts, account blocks, negative balances and write off expenses. This role will innovate creative new solutions that combine classic analytic techniques and the latest data science technologies to provide big data insights to mitigate fraud losses. This role also collaborates on large enterprise-wide projects related to all product offerings to mitigating risk, prevent loss, generating new revenue streams and create operational efficiencies. This role also manages key relationships with various data provider and data analysis vendors.
Roles and Responsibilities:
- Build, Train, Optimize & Maintain Predictive Models for the key Risk Metrics:
- Forecast card holder dispute claims
- Forecast direct claim payouts and present forecast to internal groups
- Forecast provisional credits provided to card holders
- Forecast expected agent payout performance
- Predict risky spend behavior that produces negative balances
- Forecast expected write off loss from negative balances
- Explain variance between forecasting models and actuals, present findings to leadership and recommend changes to Risk rules, processes and procedures.
- Design and maintain algorithms to optimize front line agent performance through claim assignment.
- Provide optimal triage of claim priorities to meet regulatory deadlines during peak periods while minimizing payout loss.
- Optimize negative balance recovery process to monitor for recovery errors and missed recoveries and explain any changes in behavior contributing to negative balances.
- Manage new POC projects to evaluate applications of new data science tools and technologies (i.e. Cloud computing, machine learning)
- Assist in ROI calculation for new product offerings and new card technology Design and manage controlled experiments to measure the effects of product enhancements. Assist as the SME in the validation of new data models.
- Develop survival models of customer tenure and lifetime value of accounts.
- Provide validation that new products are being used as intended and not being abused or gamed by card holders.
- Establish upper and lower bounds of expected card holder behaviors and alert on out of bound behavior.
- Develop analysis to identify Risk flags during acquisition and CIP process for ID theft and account take over.
- Provide analysis to minimize losses in customer satisfaction programs.
Qualifications and Skill Set
- 5+ years of experience as a Data Scientist or Data Analyst
- PhD or Masters in Statistics, Data Science, Computer Science or related field.
- Advanced knowledge of statistics, predictive modeling and machine learning with experience in applying techniques to solving business problems.
- Proficient with SQL and statistical software (i.e. R, Python, etc)
- Excellent communication and presentation skills; able to convey technical components to non-technical audience; able to apply scientific methods to business environment.
- Strong track record of deploying models into production with measurable impact to the business.
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