Key Tasks and Responsibilities

• Develop and deploy data-driven models and BI dashboards for early fraud detection and behavioral pattern analysis
• Conduct proactive forensic data analysis using advanced tools such as Alteryx, IDEA, Power BI, and Tableau
• Write and optimize SQL, Python, and R scripts to query structured/unstructured data for anomalies and indicators of potential fraud
• Support investigations by extracting, cleaning, and transforming large volumes of financial, claims, and transactional data
• Build repeatable analytics workflows to monitor risk indicators across key business functions
• Work closely with internal audit, risk, compliance, and IT teams to improve fraud detection strategies
• Contribute to data governance and data quality initiatives supporting forensic integrity and regulatory reporting

Skills and Competencies

• Languages/Scripting: SQL, Python, R
• Analytics & BI Tools: Power BI, Tableau, IDEA, Alteryx (Altrexy)
• Data Science & Engineering: ETL workflows, data wrangling, feature engineering
• Excel: Advanced data analysis and visualizationStrong understanding of fraud typologies in the banking and insurance sectors
• Ability to translate complex datasets into actionable forensic insights

​​​​​​​Knowledge and Experience

• Familiarity with fraud detection models (e.g., outlier detection, clustering, anomaly scoring)
• Exposure to case management tools and forensic investigation platforms
• Experience with regulatory frameworks like AML, FATCA, or fraud compliance in financial services
• Knowledge of internal audit or forensic procedures is beneficial

​​​​​​​Qualifications

• Bachelor’s degree in computer science, Information Technology, Data Science, or related field
• 1–2 years of experience in data analytics, business intelligence, or data science
• Certifications preferred:
• Power BI Certification
• Certified Data Management (CDM) or equivalent