Data Scientist - Fraud Detection

About DataVisor:
DataVisor is the world’s leading AI-powered Fraud and Risk Platform that delivers the best overall detection coverage in the industry. With an open SaaS platform that supports easy consolidation and enrichment of any data, DataVisor's fraud and anti-money laundering (AML) solutions scale infinitely and enable organizations to act on fast-evolving fraud and money laundering activities in real time. Its patented unsupervised machine learning technology, advanced device intelligence, powerful decision engine, and investigation tools work together to provide significant performance lift from day one. DataVisor's platform is architected to support multiple use cases across different business units flexibly, dramatically lowering total cost of ownership, compared to legacy point solutions. DataVisor is recognized as an industry leader and has been adopted by many Fortune 500 companies across the globe.

Our award-winning software platform is powered by a team of world-class experts in big data, machine learning, security, and scalable infrastructure. Our culture is open, positive, collaborative, and results-driven. Come join us!

Position Overview:

We are looking for a motivated Entry-Level Data Scientist to join our Fraud Detection team. In this role, you will leverage your machine learning and data analysis skills to identify fraudulent activities, build predictive models, and uncover hidden patterns in large datasets. You will work closely with cross-functional teams to develop scalable solutions that enhance our fraud detection capabilities. This is a great opportunity to grow your skills in a fast-paced, data-driven environment while making a real impact in the fight against fraud.

Key Responsibilities:

• Develop and deploy machine learning models for fraud detection and risk assessment.
• Perform exploratory data analysis (EDA) to identify trends, anomalies, and patterns in transactional data.
• Clean, preprocess, and analyze large datasets using Python and popular data science libraries (pandas, NumPy, scikit-learn, etc.).
• Collaborate with engineering and business teams to integrate ML models into production systems.
• Continuously monitor model performance and refine algorithms to improve accuracy.
• Stay updated with the latest advancements in fraud detection techniques and ML/AI technologies.