Advanced Fraud Detection and Typology Analyst

Bank of America•Jacksonville, FL

2d•Onsite

About The Position

SCCS Fraud Prevention and Detection is seeking an experienced data professional to join our team to focus on identifying emerging fraud trends, behavioral typologies, and anomalous patterns across Specialized Consumer Client Solutions LOB. This role is centered on exploratory, hypothesis‑driven analytics, working with highly complex and ambiguous data to surface new fraud risks, understand evolving attacker behavior, and inform detection strategies. The position operates as a senior individual contributor, partnering closely with executive leadership, product, technology, GIS and fraud operations to translate analytical insight into actionable detection enhancements, investigative focus areas, and strategic prioritization. The successful candidate will demonstrate strong analytical judgment, intellectual curiosity, and the ability to independently navigate incomplete or noisy data. This role is ideal for an analyst who excels at pattern recognition, clustering, anomaly detection, networked behavior analysis, and data storytelling, and who thrives in environments where the problem is not fully defined upfront.

Requirements

Demonstrated experience identifying new or evolving fraud behaviors or typologies, particularly in environments where risks are not fully defined or labeled.
Strong proficiency in SQL and at least one analytical or data science language (e.g., Python, SAS).
2+ years of experience applying data science, analytics, or advanced modeling in fraud, risk, cyber, or financial services domains.
Demonstrated exceptional attention to detail, with the ability to identify subtle data inconsistencies, behavioral anomalies, and analytical gaps that materially impact fraud outcomes.
Strong intellectual curiosity and a consistent habit of asking incisive questions to fully understand the why behind observed patterns, model behavior, and fraud outcomes.
Proven ability to think logically and structure complex problems, breaking ambiguous fraud risks into clear hypotheses, analytical approaches, and testable conclusions.
Ability to independently assess what data matters, what signals are meaningful, and what analyses are required, rather than relying on predefined scripts or instructions.
Disciplined analytical judgment, with the ability to distinguish signal from noise and avoid overfitting conclusions based on incomplete or misleading data.
Track record of producing high‑quality, defensible analysis that stands up to scrutiny from technical peers, risk partners, and senior leadership.
Experience supporting detection models or rule strategies through analytical insight.
Ability to work independently on complex, ambiguous problems with minimal direction.
Proven ability to translate analytical insights into business and risk decisions.

Nice To Haves

Bachelor’s degree in a quantitative field (e.g., Computer Science, Data Science, Statistics, Engineering, Mathematics) or equivalent practical experience.
Experience working in fraud discovery, advanced detection analytics, or threat research style roles.
Familiarity with graph‑based thinking or networked fraud behaviors (even if tools were indirect).
Advanced degree (master’s or higher) in a quantitative discipline.
Experience with fraud detection use cases such as account takeover, market manipulation, synthetic identity, payments fraud, or digital abuse.
Familiarity with model risk management frameworks and regulatory expectations (e.g., SR 11‑7).
Experience working with largescale, distributed data environments.
Prior exposure to vendor model evaluation, proof‑of‑concept testing, or platform integrations.
Strong executive presence and comfort influencing senior leaders without direct authority.

Responsibilities

Develops and implements long-term strategies for a business area
Manages complex projects from initiation to delivery ensuring milestones and outcomes are met
Identify and mitigates risk to safeguard project and business objectives
Influences decision-making and facilitates alignment across teams
Drives innovation by championing change and continuous improvement
Identify emerging fraud typologies, behavioral patterns, and anomalous activity through deep exploratory analysis of large, complex datasets.
Perform trend analysis, clustering, segmentation, and grouping techniques to uncover non‑obvious relationships and evolving fraud behaviors.
Analyze transactions, customer behavior, devices, accounts, and networked entities to surface signals indicative of novel or previously undetected fraud.
Develop clear hypotheses in ambiguous problem spaces and iterate analytically as new findings emerge.
Serve as a technical thought leader within Fraud Detection, guide and influence other business leaders and peer SMEs rather than formal management responsibility.
Maintain awareness of industry fraud trends, emerging technologies, and regulatory expectations, proactively adapting analytical approaches.
Translate analytical findings into actionable insights for fraud strategy, detection rules, models, and investigative prioritization.
Partner with Fraud Strategy, GIS, Client Protection, Digital and Technology teams to support downstream model enhancements, detection logic, and control changes.
Create compelling visualizations, summaries, and executive‑ready narratives to communicate complex findings clearly.
Conduct root‑cause and driver analysis on material fraud events, spikes, or shifts in loss performance.
Evaluate new data elements and signals for relevance to detection and typology development.