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# Lead Data Scientist

- **Company:** PAYMENT SYSTEMS OF OHIO, LLC
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Big Data, Fraud Prevention and Detection, Python (Programming Language), Machine Learning, Transaction Data, Unstructured Data, System Availability, Stream Processing, Data Pipelines
- **Published:** August 24, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/pelfdbfk7v

## About the Role

* Around 5 years or more experience in data science or machine learning
 * Strong experience with Python and common data science libraries
 * Experience with anomaly detection, fraud detection, or similar domains is preferred
 * Experience working with large datasets in production environments
 * Understanding of data pipelines and working with structured and unstructured data
 * Experience with AWS infrastructure as a foundation for building and deploying data science solutions
 * Ability to work in a greenfield setup with limited predefined structure
 * Strong problem-solving mindset and ownership
 * Excellent English skills

Nice to have

 * Experience in payments, fintech, or iGaming
 * Exposure to real-time or near real-time data processing
 * Experience working in cross-functional teams with product and engineering

## Description

PaymentIQ is building a dedicated data team in Bitola with a clear purpose: to leverage large-scale transaction data and turn it into actionable intelligence. As a Lead Data Scientist, you will take ownership of building fraud detection and anomaly detection capabilities from the ground up, while guiding the team in experimentation, model development, and delivery.

This is a greenfield opportunity where no existing models are in place yet. You will define direction, select approaches, and ensure that data science efforts translate into real product value., * Lead the design and development of fraud detection and anomaly detection models

 * Work with large-scale payment transaction data to identify patterns and risks
 * Define and guide experimentation with machine learning models
 * Collaborate with Data Engineers on data pipelines and data availability
 * Align closely with Product on roadmap items and priorities
 * Translate business problems into data science solutions
 * Support and mentor team members as the team grows
 * Contribute hands-on to model development and validation


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