Principal II - Fraud Analytics Lead; Consulting Manager

What You Will Do

• Establish analytical strategies, methodologies, and priorities supporting fraud analytics, predictive analytics, forensic analysis, data mining, and cybersecurity monitoring initiatives.
• Develop and apply in-depth analyses of complex data to identify fraudulent activity, anomalous behavior, root causes, emerging risks, and potential attack vectors.
• Exercise independent judgment in selecting analytical methods, techniques, tools, and evaluation criteria to obtain results and inform decision-making.
• Provide technical leadership, mentorship, and strategic direction across analytical work streams while reviewing and approving complex analytical products and recommendations.
• Define, refine, and oversee implementation of fraud indicators, detection methodologies, predictive models, analytical frameworks, and statistical analysis approaches.
• Collaborate with stakeholders to integrate data mining results with existing systems, improve analytics performance, and recommend process or technology enhancements.
• Prepare and present technical briefings, reports, executive summaries, dashboards, and recommendations based on analytical findings and operational priorities.
• Support data ingestion, schema alignment, data quality review, extract, transform, load activities, and analytics workflow improvements.
• Identify alternative applications for existing tools, techniques, and technologies to improve fraud analytics outcomes and operational effectiveness.
• Advise government and contractor leadership on fraud trends, emerging risks, analytical priorities, and opportunities to improve fraud detection effectiveness.
• Work onsite at the client location in Lanham, Maryland five (5) days per week and collaborate closely with government personnel and contractor team members.

Data Science & Analysis

None

Ability to Obtain Public Trust

• Ability to obtain and maintain a Public Trust clearance.
• Bachelor's degree in Data Science, Computer Science, Cybersecurity, Information Systems, Statistics, Mathematics, Engineering, Operations Research, Information Science, or a STEM related field.
• EIGHT (8) or more years of professional experience supporting fraud analytics, cybersecurity analytics, data science, data engineering, predictive analytics, incident response, investigations, platform advisory, or related technical delivery.
• Experience leading analytical work streams, technical tasks, or program segments in data analytics, fraud analytics, cybersecurity, systems integration, or platform advisory environments.
• Experience establishing analytical strategies, technical approaches, or fraud detection methodologies across multiple teams or work streams.
• Experience developing and applying in-depth analysis of complex data using professional concepts, methodologies, and independent judgment.
• Experience with machine learning, statistical modeling, predictive analytics, data mining, link analysis, network analytics, text mining, data visualization, or related analytical techniques.
• Experience preparing and presenting technical reports, briefings, dashboards, recommendations, or executive-ready analytical products.
• Experience providing technical leadership, direction, or mentorship to team members completing analytics, data integration, documentation, or investigative tasks.
• Experience using tools and technologies such as Python, R, Java, Linux shell scripting, SQL/no-SQL databases, Spark, Elasticsearch, Parquet, SIEM platforms including Splunk, Microsoft Word, Microsoft Excel, and Microsoft PowerPoint.
• Experience with web development languages and frameworks such as HTML, Python/Flask, JavaScript, CSS, jQuery, or React in support of platform advisory, analytics ecosystem, data/application integration, dashboard, or application-facing analytics work.
• Ability and willingness to work onsite at the client location in Lanham, Maryland five (5) days per week.

• Experience leading fraud analytics, cybersecurity analytics, data science, or incident response teams in a federal or large enterprise environment.
• Experience supporting online application monitoring, identity verification, digital identity, credential service provider integrations, or large-scale transaction monitoring environments.
• Experience developing or guiding machine learning models, predictive models, statistical models, analytical algorithms, fraud indicators, or detection logic.
• Experience with SIEM platforms, including Splunk,…