Data Scientist 2-3 - Card Fraud Analytics

at  Truist Bank

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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.

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 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
3. 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
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

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 decompensations, principal components, working knowledge of calculus/differential equations, with understand 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 )
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

ESSENTIAL DUTIES AND RESPONSIBILITIES

Following is a summary of the essential functions for this job. Other duties may be performed, both major and minor, which are not mentioned below. Specific activities may change from time to time.
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 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.

ENTRY-LEVEL RESPONSIBILITIES

Supports fraud strategy function, develops 3 or more fraud rules per month while conducting analyses to identify underperforming fraud strategies that need to be retired. In supporting rule analysis, Teammate should be able to Independently perform sophisticated data analytics that fit to the fraud problem being observed (ranging from classical econometrics to machine learning, neural networks, and natural language processing) in a variety of environments using structured and unstructured data. Produce compelling data visualizations to communicate insights and influence outcomes among a wide array of stakeholders
Take accountability and ownership of end-to-end data science solution design, technical delivery, and measurable business outcomes. 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.
Use, maintain, share and collaborate through Truist internal code repositories to foster continual learning and cross-pollination of skillsets.
Actively research and advocate adoption of emerging methods and technologies in the data science field, with the eye of continually advancing Truist’s
capabilities. 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.