Lead Data Analyst – Enterprise Fraud Risk Management (Remote)
Position Title:
Lead Data Analyst – Enterprise Fraud Risk Management Location: Fully Remote (Client based in Washington, DC)
Employment Type: Contract / Full-Time (based on engagement)
Position Overview:
Randstad is seeking an experienced Lead Data Analyst to join the Enterprise Fraud Risk Management (EFRM) team supporting a major transit and infrastructure client. This fully remote opportunity offers the chance to become a key contributor in a rapidly expanding fraud risk program focused on identifying, assessing, and monitoring over 180 enterprise fraud risks across procurement, cybersecurity, financial reporting, and operational functions. The ideal candidate is a highly analytical, proactive professional who excels in exploratory data analysis, anomaly detection, and data discovery within complex and evolving environments. This role requires someone who can build analytical processes from the ground up, uncover hidden fraud indicators, and collaborate across multiple business functions to strengthen enterprise fraud prevention and detection capabilities.
Key Responsibilities
Fraud Analytics & Data Discovery:
Perform deep-dive exploratory analytics to identify anomalous patterns, trends, and potential indicators of fraud.
Analyze structured and unstructured datasets to uncover internal and external fraud risks.
Conduct advanced data mining and investigative analysis across procurement, cyber, finance, and operational domains.
Develop and execute data discovery methodologies to proactively identify emerging fraud vectors.
Program Development & Process Design:
Establish and refine data discovery frameworks, workflows, and documentation standards within a greenfield fraud program.
Design scalable processes for identifying, documenting, and escalating potential fraud cases for formal investigation.
Create repeatable analytical approaches and governance practices to support long-term fraud risk management.
Cross-Functional Collaboration:
Partner closely with Digital Technology (DT), Governance, Risk & Compliance (GRC), Internal Audit, Procurement, Finance, and Cybersecurity teams.
Translate business risks into analytical use cases and data-driven monitoring strategies.
Collaborate with stakeholders to understand data structures, business processes, and fraud risk indicators.
Strategic Analytics & Advisory:
Provide recommendations on analytical approaches, fraud detection methodologies, and process improvements.
Adapt quickly to changing priorities, evolving datasets, and new investigative requirements.
Present findings, insights, and recommendations to leadership and key stakeholders.
Contribute to the strategic direction of enterprise fraud analytics initiatives.
Required Qualifications:
Bachelor's degree in Data Analytics, Statistics, Computer Science, Information Systems, Finance, Accounting, or a related field.5+ years of experience in data analytics, data discovery, advanced data mining, or investigative analytics.
Experience supporting fraud detection, fraud risk management, forensic analytics, or anomaly detection initiatives.
Strong ability to work independently in highly ambiguous and evolving environments.
Proven experience developing analytical processes and frameworks from concept to implementation.
Excellent problem-solving, communication, and stakeholder management skills.
Ability to present complex analytical findings to technical and non-technical audiences.
Preferred Qualifications:- Experience with procurement fraud, cyber fraud, financial statement fraud, or internal investigations.
- Prior experience within construction, transportation, infrastructure, utilities, engineering, or other asset-intensive industries.
- Familiarity with enterprise risk management, governance, compliance, or internal audit functions.
- Experience working in large-scale organizations with complex data ecosystems.
- Knowledge of fraud risk assessment methodologies and investigative workflows.
- Technical Skills
- Preferred experience with:
- SQL and advanced querying techniques
- Python or R for data analysis and statistical modeling
- Data visualization tools (Power BI, Tableau, or similar)
- Data mining, anomaly detection, and pattern recognition techniques
- Enterprise data platforms and large-scale data environments
- Documentation and workflow management tools
- Success Profile
- Naturally curious and investigative.
- Comfortable operating with limited structure and evolving requirements.
- Skilled at uncovering hidden insights within large, complex datasets.
- Confident in challenging assumptions and proposing innovative analytical approaches.
- Effective at balancing technical execution with strategic business impact.
- Why This Opportunity?
- Join a high-visibility enterprise fraud risk program at an early growth stage.
- Directly influence how fraud risks are identified, analyzed, and escalated across the organization.
- Work remotely while collaborating with senior stakeholders across multiple business functions.
- Contribute to the development of innovative fraud detection and risk management capabilities that support a critical public infrastructure organization.
