Data Scientist II – Identity Protection

Key Skills

Java Programming, Finance, JQuery, Python Programming, SQL, Agile methodologies, Apache Hadoop, Machine learning techniques, Data science techniques, Data Mining, Data Visualization, NoSQL, Tableau, Apache Hive, Apache Pig, SPARK Programming

Job Description

We are seeking a motivated candidate who is experienced in performing sophisticated analytics (statistical and predictive analytics, machine learning modeling, etc.) to solve complex problems and displays a passion for stopping bad actors and protecting our customers.

In this role you will utilize multiple data sources and analytical tools to provide actionable insights that improve business outcomes and develop effective fraud risk management strategies to mitigate fraud loss while ensuring an appropriate balance between risk tolerance and customer experience. You must be familiar with data analysis techniques, software, and processes, and you should possess the creative problem-solving abilities necessary to suggest new methods of analysis and prevention. This job will support real-time fraud detection rule systems for a variety of monetary and non-monetary transactions including Deposit Account Applications, Credit Card Applications, Account Takeover, Mortgage Lending Products and Data Breach.

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.

LEVEL II:

• Independently identify fraud patterns and trends utilizing advanced analytical techniques, data mining, machine learning and visualization dashboards with SAS, Python, Tableau
• Develop, validate, and implement fraud strategies logic to mitigate financial loss and improve the client experience.
• Make decisions in a timely manner while balancing the need for action with the need for data analysis.
• Develop key fraud metric reporting and ongoing monitoring routines.
• Communicate fraud related incidents to key stakeholders.
• Participate in the development of business cases to support fraud strategic initiatives
• Support the company’s commitment to risk management and protecting the integrity and confidentiality of systems and data.

• Demonstrated strong mastery of data science or check fraud knowledge to proactively lead analytic projects.

• Work experience with fraud systems such as Actimize, LexisNexis, Early Warning Services, SAS, FCMS

• Proven experience developing new fraud rules for either fast moving fraud attack trends with attention to detail for implementation into a production environment that minimizes mistakes in rule logics

• 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

• Successful in setting up automation in support of recurring fraud routines / analytics.

• Strong skill set with experience using multiple machine learning models provided by vendors and/or inhouse development.

QUALIFICATIONS

LEVEL II:

• 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.
• 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
• Exhibit understanding of statistical methods, including a broad understanding of classical statistics, probability theory, econometrics, time-series, and primary statistical tests
• 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
• Demonstrate understanding of data cleansing and preparation methodologies, including regex, filtering, indexing, interpolation, and outlier treatment
• Strong familiarity with data extraction in a variety of environments (SQL, JQuery, etc.)
• Working knowledge of SAS, Python, Hadoop, Pig, Hive, and/or NoSQL, Spark
• Experience in managing multiple projects with tight deadlines in a collaborative environment

Preferred Qualifications:

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

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.