Data Science Manager

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 solution scales infinitely and enables 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 guaranteed performance lift from day one. The flexible architecture of DataVisor's platform allows enterprises to power sophisticated and complex use cases across different businesses while dramatically lowering the total cost of ownership. DataVisor is recognized as an industry leader and has been adopted by Fortune 500 companies globally across many industries.

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 Data Science Manager or Director to lead the design and deployment of advanced machine learning models for fraud detection common use cases across financial industries,  such as ACH, card payments, onboarding, and more. This role is central to DataVisor’s mission to stop fraud using cutting-edge AI.

You will drive the development of industry-level consortium models as well as customized models tailored to individual clients. Your work will span supervised and unsupervised learning, anomaly detection, and large language models (LLMs), with a focus on using AI to accelerate data analysis, result interpretation, and fraud signal discovery. This is a hands-on leadership role that combines deep technical expertise with strategic thinking and customer engagement.

Key Responsibilities

Advanced ML Model Development

• Lead the end-to-end development of machine learning models, including supervised, unsupervised, and deep learning approaches, for fraud detection and risk mitigation.
• Incorporate large language models (LLMs) and generative AI to improve detection precision, result explainability, and analytical workflows.
• Design and deliver industry-wide consortium models as well as client-specific models to meet both broad and tailored detection needs.
• Enhance and scale the model governance frameworks, including model documentation, version control, validation, monitoring, and regulatory compliance alignment.

AI-Powered Data Analysis and insight derivation

• Use AI to assist with data mining, fraud trend detection, and root cause analysis for fraud events.
• Automate result interpretation, detection reporting, and ongoing model performance diagnostics and explanations.

Client-Centric Solutions

• Collaborate with top-tier financial institutions, fintechs, and e-commerce platforms to understand fraud challenges and deliver measurable detection improvements.
• Translate client-specific data and feedback loops (e.g., chargebacks, ACH returns) into actionable machine learning strategies.

Cross-Functional Execution

• Partner with Engineering and Product teams to operationalize models in production, ensuring high scalability and performance.
• Support Customer Success teams with detection insights and model tuning.

Thought Leadership & Research

• Publish and present on fraud trends, model innovations, and AI applications.
• Represent DataVisor in external forums, industry conferences, and customer workshops.

• PhD in Computer Science, Statistics, Machine Learning, or a related quantitative field.
  7+ years of experience applying machine learning to real-world problems, preferably in fraud/risk domains.
• Deep understanding of fraud data ecosystems and feedback mechanisms (e.g., chargebacks, disputes, returns) is a plus
• Strong programming and analytical skills (Java, Python, SQL, scikit-learn, XGBoost, PyTorch/TensorFlow), with pragmatic approaches to solve real-world problems
• Experience with LLMs and generative AI applications in detection or analytics is a strong plus.
• Proven ability to build, deploy, and monitor ML models in a production environment.
• Strong communication and collaboration skills with the ability to work across technical and non-technical teams, including direct engagement with clients.
• Leadership experience in hiring/mentoring/managing data scientists and driving cross-functional initiatives.

Health Insurance, PTO, 401k