Fraud Data Analytics Lead, (Senior Consultant, Data & Analytics)

Fearless•Baltimore, MD

5d•$120,588 - $195,955•Remote

About The Position

At Fearless, we are seeking a Fraud Data Analytics Lead as part of our Senior Consultant, Data and Analytics career path to drive program integrity and fraud prevention efforts for federal government programs. This role combines deep fraud analytics expertise with technical solution design, translating complex program integrity requirements into actionable detection models and system integrations. You will serve as the bridge between business stakeholders and engineering teams, advising on data strategy, fraud monitoring capabilities, and emerging threats in identity verification and fraud prevention. This is a fully remote role. At Fearless, the Senior Consultant is an expert in data analysis, engineering, and science. This role will possess the necessary skills at an advanced level of proficiency to drive organizational transformation by architecting scalable data platforms, building advanced machine learning solutions, and mentoring customers and teams to become data-driven organizations. The role involves working with executives, project teams, and internal leaders across departments to identify opportunities where data can accelerate missions, create efficiencies, and deliver technical excellence.

Requirements

Minimum of 10 years of experience in fraud analytics, program integrity, or related data analysis roles supporting federal programs.
Bachelor’s degree in Data Science, Statistics, Computer Science, or a related field, or equivalent work experience.
Ability to obtain and maintain a Public Trust clearance.
U.S. Citizenship is required.
Proven experience developing fraud detection models and conducting data analytics to identify suspicious activity and trends.
Experience with SQL, Python, and analytics tools to query, manipulate, and analyze large datasets.
Strong understanding of fraud, waste, abuse, and identity management concepts within program integrity frameworks.
Demonstrated ability to translate business and program integrity requirements into technical solutions and system integrations.
Experience working independently in ambiguous environments.
Strong written and verbal communication skills.
Ability to explain complex analytical findings and technical decisions to both technical and non-technical stakeholders.
Ability to adapt to evolving fraud threats, changing priorities, and new technologies.

Nice To Haves

Experience with data visualization tools such as Tableau to build dynamic dashboards and reporting solutions.
Familiarity with relational and non-relational database systems and database architecture.
Knowledge of emerging fraud threats introduced by AI and evolving technologies in identity verification and fraud prevention.

Responsibilities

Develop and maintain fraud analytic models to detect identity fraud, account takeover, synthetic identity fraud, and emerging fraud patterns.
Conduct analytics across enrollment, registration, application, and investigative data to identify suspicious behaviors, fraudulent trends, and emerging fraud patterns across federal partner ecosystems.
Collaborate with DevOps and SecOps teams to improve data infrastructure and operationalize analytics programs at scale.
Monitor fraud analytics, including entity resolution, social network analysis, and unsupervised monitoring, to establish anomaly behaviors and alert triggers.
Incorporate data from third-party fraud detection tools and continuously refine fraud detection models as fraud schemes evolve.
Develop dashboards and reporting solutions that communicate key performance indicators, suspicious activity alerts, and fraud trends to stakeholders and leadership.
Conduct fraud risk assessments to identify program vulnerabilities, establish risk tolerances, and develop mitigation strategies and response plans.
Advise on data collection, analysis, and visualization strategies to support program integrity decision-making and technical roadmaps.
Utilize data-driven insights and fraud trend analysis to inform program strategy, optimize fraud detection policies and third-party tool configurations, improve operational processes, and guide staffing and technology investment decisions.
Continuously monitor, evaluate, and optimize fraud detection model performance, refining detection logic as fraud tactics evolve to maintain effectiveness and minimize false positives.
Define, monitor, and report fraud analytics metrics, including alert volumes, adjudication outcomes, response times, fraud trends, and other operational performance indicators.
Develop fraud response plans, including escalation procedures, scenario planning, and operational readiness activities to support effective response to emerging fraud events.