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Fraud Modelling/Forecasting Specialist (with Data Graph Expertise)
This job is now closed
Job Description
- Req#: R_1388923
Employer Industry: Financial Services
Why consider this job opportunity:
- Salary up to $155,376
- Opportunities for career advancement and professional development within the organization
- Remote work flexibility with less than 10% travel required
- Comprehensive benefits package including health, well-being, and retirement programs
- Supportive work environment that promotes teamwork and innovation
- Chance to work with cutting-edge technologies in fraud detection and analytics
What to Expect (Job Responsibilities):
- Develop and implement innovative fraud detection models and strategies using graph theory and advanced analytics
- Collaborate with cross-functional teams to design and monitor fraud mitigation solutions
- Analyze complex data structures and identify trends to enhance fraud detection capabilities
- Lead activities related to the delivery of data models and mappings, ensuring alignment with business needs
- Conduct meaningful analysis to assess efficiency levels within the function and implement improvements
What is Required (Qualifications):
- Undergraduate degree or advanced technical degree preferred (e.g., math, physics, engineering, finance, or computer science)
- 5+ years of relevant experience in fraud detection or analytics
- Strong knowledge of graph algorithms and practical applications in fraud detection
- Experience with graph databases such as TigerGraph or Neo4j, and cloud platforms like Databricks
- Expertise in building machine learning models for fraud detection
How to Stand Out (Preferred Qualifications):
- Advanced degree in a relevant field with project work experience
- Familiarity with data analysis and visualization tools such as Python, R, SQL, Tableau, or Power BI
- Experience with distributed computing platforms like Spark for big data processing
- Knowledge of different types of fraud, including account takeover and money laundering
- Ability to mentor and share knowledge within the team
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