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

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

The 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 I:

1. Independently identify fraud patterns and trends utilizing advanced analytical techniques, data mining, machine learning and visualization dashboards with SAS, Python, Tableau

2. Develop, validate, and implement fraud strategies logic to mitigate financial loss and improve the client experience.

3. Make decisions in a timely manner while balancing the need for action with the need for data analysis.

4. Develop key fraud metric reporting and ongoing monitoring routines.

5. Communicate fraud related incidents to key stakeholders.

6. Participate in the development of business cases to support fraud strategic initiatives

7. Support the company’s commitment to risk management and protecting the integrity and confidentiality of systems and data.

Essential Duties and Responsibilities

The 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.

1. 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.

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

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

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

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

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

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

8. Exercise sound judgment and foster 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

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

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 SAS, Python, 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