Card Fraud Data Scientist II
Job in
Atlanta, Fulton County, Georgia, 30383, USA
Listing for:
Cooper Lighting Solutions
Full Time
position
Listed on 2026-06-17
Job specializations:
-
IT/Tech
Data Analyst, Data Scientist, Data Science Manager, Data Security
Salary/Wage Range or Industry Benchmark: 100000 - 125000 USD Yearly
USD
100000.00
125000.00
YEAR
Job Description & How to Apply Below
Language Fluency
English (Required)
Work Shift
1st shift (United States of America)
Job Grade
110
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.
For this opportunity, Truist will not sponsor an applicant for work visa status or employment authorization, nor will we offer any immigration-related support for this position (including, but not limited to H-1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN-1 or TN-2, E-3, O-1, or future sponsorship for U.S. lawful permanent residence status.)
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
Required 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.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 Hadoop, Pig, Hive, and/or No
SQL, 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.
Additional
Job Description Experience analyzing fraud trends using large datasets, applying statistical techniques, SQL, and/or data‑science tools to identify anomalies, build rules, or evaluate model performance.
Working experience with real-time fraud decisioning platforms such as Falcon, Actimize, SAS Fraud Management, or similar machine‑learning–driven systems.
Hands‑on expertise with fraud detection tools and data signals, including device intelligence, behavioral biometrics, consortium data, third‑party risk scoring, and velocity or link‑analysis tools.
Proven ability to translate fraud risk requirements into actionable rules, model features, detection logic, or platform configurations.
Strong understanding of payment…
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