Job Description

Regular or Temporary:

Regular

Language Fluency: English (Required)

Work Shift:

1st shift (United States of America)



Please review the following job description:

Perform sophisticated analytics (statistical and predictive analytics, machine learning modeling, etc.) to provide actionable insights that improve business outcomes and minimize risk and also provide consultation to business leaders and other stakeholders on how to leverage analytics insights and build strategies around analytics.

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.

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. With minimal guidance, 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 Truists capabilities.
8. 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.

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. Bachelors 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 Hadoop, Pig, Hive, and/or NoSQL, Spark
7. Experience in managing multiple projects with tight deadlines in a collaborative environment

Preferred Qualifications:

1. Masters 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