Position Details
About this role
Principal Associate Data Scientist role focused on fraud prevention using knowledge graphs and graph algorithms. You will partner with cross-functional teams to build, validate, and implement graph-based machine learning solutions.
Key Responsibilities
- Leverage Python, Conda, AWS, Spark, Gremlin, NeptuneDB to build knowledge graphs and graph algorithms
- Pilot graph modeling algorithms across design, training, evaluation, validation, and implementation
- Connect modeling expertise to fraud prevention business goals
- Partner with cross-functional teams to deliver fraud defenses
- Improve customer protection data science solutions
Technical Overview
Build knowledge graphs and graph algorithms using Python, SQL, and AWS, leveraging tools such as Conda, Spark, Gremlin, and NeptuneDB. Drive model development end-to-end from design through training, evaluation, validation, and implementation for production fraud defenses.
Ideal Candidate
The ideal candidate is a data scientist with strong hands-on experience building knowledge graphs and graph algorithms, using Python and SQL with cloud computing on Amazon Web Services. They are experienced across the full model lifecycle (design, training, evaluation, validation, and implementation) and apply machine learning techniques to fraud prevention at scale.
Must-Have Skills
Nice-to-Have Skills
Tools & Platforms
Required Skills
Hard Skills
Soft Skills
Industry & Role
Keywords for Your Resume
Deal Breakers
Must have hands-on experience with Python and SQL, Must have experience using AWS (Amazon Web Services)
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