Data Scientist II

Truist Bank•Atlanta, GA

1d•Onsite

About The Position

The Data Scientist II position involves performing sophisticated analytics, including statistical and predictive modeling, and machine learning to derive actionable insights that enhance business outcomes and mitigate risk. This role also requires consulting with business leaders and stakeholders on leveraging these insights and developing data-driven strategies. Truist will not sponsor an applicant for work visa status or employment authorization for this position.

Requirements

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 NoSQL, Spark
Experience in managing multiple projects with tight deadlines in a collaborative environment
English language fluency

Nice To Haves

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
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 ecosystems, including card network rules, issuer/acquirer authorization flows, chargeback lifecycle, and dispute management.
Background in fraud strategy design, including rule optimization, model monitoring, and roadmap planning.
Experience working with cross‑functional teams (Product, Engineering, Compliance, Operations) to implement fraud‑risk initiatives.

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.