Data Scientist I - Card Fraud Analytics - Atlanta, GA

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Regular or Temporary:

Regular

Language Fluency:  English (Required)

Work Shift:

1st shift (United States of America)

Please review the following job description:

Please review the following job description:

Perform sophisticated card fraud analyses and strategy support using advanced analytics & data science tools to provide actionable insights and real-time changes to card fraud detection tools that improve business outcomes and minimize fraud risk.  A key part of the role will be regularly providing active and guiding consultation to business leaders and other stakeholders on best methods of leveraging analytics and fraud strategies to reduce gross fraud while maintaining a world class customer experience for active card purchasing customers using our credit and debit card payment products at Truist Bank.  This role will actively support multiple real-time fraud detection rule systems where the role will be actively be involved with building, developing , and demonstrating a mastery of card , fraud , and advanced analytics combined skill sets in an area with an abundance of vast/rich data features to work with.

Level II – 110

ESSENTIAL DUTIES AND RESPONSIBILITIES

Additional Responsibilities for Grade 110

1.  Proactively takes initiative to organize, lead, and coach new peers, and non-card team members in support of growing the overall fraud, card, and/or analytical techniques and knowledge base.

2.  Demonstrated Strong Mastery of Card network data science origins from VISA / Mastercard and the established infrastructure to support payment processing between issuers, networks, and card processors

3. Work experience with one or more of the following card processors and/or rules systems:  First Data Defense Edge / FDWC, VISA DPS Falcon Expert / Case Manager, FIS Falcon Expert / Case Manager, Tsys Card Guard / Determinator, Broadcom Risk Analytics, Visa Risk Manager

4.  Demonstrated fraud knowledge with proven experience developing new fraud rules for either fast or slow moving fraud attack trends with attention to detail for implementation of rules into a production environment that minimizes mistakes in rule logics

5.  Demonstrated capability with at least one or more analytics products with demonstrated skill to build complex queries into big data environments using approved low impact to technology environment tools

6.  Proven capability to build / develop new business intelligence type tools for use in fraud detection or recovery from inception using fraud and/or card fraud intelligence.

7.  Successful in setting up scheduled jobs in support of recurring fraud routines / analytics that then can be used to support a wider team of fraud analytics and reporting support team members.

8.  Demonstrated Experience working with both authentication and authorization data types with capability to demonstrate best practices for integrating fraud matching data science for large data sets that have no relational keys

9.  Strong skill set with experience using multiple machine learning models provided by vendors and/or inhouse development while working with appropriate enterprise model risk partners to identify if models are being used optimally to reduce gross fraud while minimizing client experience impacts

10.  Supports the interpretation of GL fraud reporting as it relates to detected fraud, chargebacks, and fraud charge-offs and the differences that occur between debit card and credit cards.

11.  Independently perform sophisticated fraud detection monitoring of fraud patterns using advanced analytics and dashboarding tools such as SAS, Python, Tableau, Excel pivot to assist in the identification and mitigation of card fraud trends.

12.  Develop new queries , data mining, data insights through use of basic and complex SQL queries statements to our IBM and/or datalake environments that involve querying big data necessary to support analyses in the lower analytical environments

13.  Build analytical tools that provide faster visibility to emerging workforce management imbalances by quardrants of risk for alerts generated by fraud rules that are distributed to queues worked by fraud operations partners both onshore and offshore ( High / Medium / Low )

14.  Utilize Mastercard and Visa fraud benchmarking tools to understand opportunities to further improve fraud performance with respect to approval rates, and gross / net fraud basis points

15.  Develop weekly time series and compelling data visualizations to organize fraud run rates and pareto charts for both eligible and ineligible fraud trends being observed with analytical tools designed to rapidly identify emerging / new fraud patterns while presenting the outcomes to a wide array of stakeholders

16.  From new fraud patterns identified, demonstrate capability to take fast actions to stop new fraud trends as they emerge using rule platforms and alerting capabilities through demonstrating value of identifying a fraud forecast and false positive rate of the pattern identified

17.  Build out advanced segmentation capabilities for assigned card products and methods of card use to support reduction of false declines and where current fraud rules contribute to elevated false positive rates

Original Job Responsibilities for Grade 109

18.  Independently perform sophisticated data analytics (ranging from classical econometrics to machine learning, neural networks, and natural language processing) in a variety of environments using structured and unstructured data

19.  Produce compelling data visualizations to communicate insights and influence outcomes among a wide array of stakeholders

20.  Take accountability and ownership of end-to-end data science solution design, technical delivery, and measurable business outcomes

21.  Engage in stakeholder meetings to identify business objectives and scope solution requirements

With minimal guidance, write, document, and deploy custom code in a variety of environments (Python, SAS, R, etc.) to create predictive analytics applications.

22.  Use, maintain, share and collaborate through Truist internal code repositories to foster continual learning and cross-pollination of skillsets.

23.  Actively research and advocate adoption of emerging methods and technologies in the data science field, with the eye of continually advancing Truist’s capabilities.

24.  Exercise sound judgment and fosters risk management culture throughout design, development, and deployment practices; partner with cross-functional teams to coordinate rules on data usage, data governance and analytics capabilities.

QUALIFICATIONS
Required Qualifications:  

The requirements listed below are representative of the knowledge, skill and/or ability required.  Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
1.    Bachelor’s degree and zero to four or more years of experience in a quantitative field such as Finance, Mathematics, Analytics, Data Science, Computer Science, or Engineering, or equivalent education and related training 
2.    Exhibit understanding of statistical methods, including a broad understanding of classical statistics, probability theory, econometrics, time-series, and primary statistical tests
4.    Demonstrate understanding of data cleansing and preparation methodologies, including regex, filtering, indexing, interpolation, and outlier treatment
5.    Strong familiarity with data extraction in a variety of environments (SQL, JQuery, etc.)
6.    Working knowledge of Hadoop, Pig, Hive, and/or NoSQL, Spark
7.    Experience in managing multiple projects with tight deadlines in a collaborative environment

8.    Working knowledge and/or hands on fraud detection experience with card products, card processors, card networks, and one or more fraud rule systems such as Defense Edge, FDWC, Falcon Expert, Tsys Card Guard, Tsys Determinator, Broadcom 3DS, Token Administration, Visa Risk Manager

Preferred Qualifications: 
1.    Master’s degree or PhD in a quantitative field such as Finance, Mathematics, Analytics, Data Science, Computer Science, or Engineering
2.    Four years of relevant work experience if candidate lacks graduate degree
3.    Previous experience in the banking or fin-tech industry

4.    Familiarity with linear algebra concepts for optimization, complex matrix operations, eigenvalue decompositions, and principal components; working knowledge of calculus/differential equations, with understanding of stochastic processes

5.    Participant at Card Fraud benchmarking forums with deep industry knowledge to help our team advance with modern fraud strategies.

6.  Capability to develop or leverage the advanced analytics techniques of machine learning fraud rule generation to refit our top 20% of fraud rules per fraud rule platform

7.  Experience with working with geo-location data-sets used to identify patterns of fraud ( online and/or offline )

Truist supports a diverse workforce and is an Equal Opportunity Employer who does not discriminate against individuals on the basis of race, gender, color, religion, national origin, age, sexual orientation, gender identity, disability, veteran status or other classification protected by law. Drug Free Workplace.

Thank you for your interest in Truist! BB&T and SunTrust have come together in a transformational merger of equals to create Truist, the premier financial organization in the country. You may notice references to our legacy company names, BB&T and SunTrust, in places throughout this site. All such references should be understood to refer to Truist moving forward while we continue to transition to the Truist name.

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