Fraud Data Scientist II

Essential Duties And Responsibilities

• 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.
• 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 outcome.
• Engage in stakeholder meetings to identify business objectives and scope solution requirements.
• Independently 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 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

  • 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.
  • Show 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.
  • Show strong familiarity with data extraction in a variety of environments (SQL, JQuery, etc.).
  • Show experience in managing multiple projects with tight deadlines in a collaborative environment.