Data Scientist

ABOUT REPAY

REPAY ("Realtime Electronic Payments" / NASDAQ TICKER: RPAY) is an established and fast-growing publicly traded financial technology and payment processing company headquartered in Atlanta, Georgia, with offices across the country. REPAY enables its customers to accept payments anytime, anywhere, and through any channel while providing a secure, seamless, and enjoyable payment experience for the end consumers. REPAY offers a comprehensive suite of electronic payment and funding solutions, including debit and credit card processing, ACH processing, Instant Funding, and electronic bill payment systems with full IVR, text, and mobile capabilities.

The scalability of its products allows merchants of all sizes to add an instant arsenal of intelligent payment technology solutions to their businesses without significant development costs or infrastructure investments.

REPAY is looking for a Data Scientist to join our growing team. The Data Scientist is responsible for designing, building, and operationalizing intelligent systems that extract value from data and power ML and AI-driven product capabilities. This role spans data science, applied AI (including generative AI and LLM-based systems), production-grade machine learning engineering and advanced analytics. The Data Scientist partners with Product, BI, and Data Engineering teams to establish data science and machine learning standards, architect scalable AI systems, and deliver measurable business impact across internal and customer-facing solutions.

• Deliver actionable, data-driven insights and reporting to inform business decisions and evaluate performance across products, operations, and AI systems.
• Design and maintain scalable data-to-AI pipelines covering ingestion, transformation, feature/prompt engineering, model training, orchestration, deployment, and monitoring.
• Deliver AI-driven solutions that measurably improve key product or operational metrics (e.g., revenue uplift, cost reduction, consumer satisfaction, platform efficiency, prediction accuracy, latency reduction).
• Partner with Data and Product to identify and execute AI opportunities aligned with strategic objectives.
• Establish and enforce AI and machine learning and data operational standards, governance, and best practices across the organization.
• Build reliable experimentation frameworks to validate model performance and business impact and drive iterative improvements through reliable model evaluation and testing.
• Own the scalability, robustness, observability, optimization and operational excellence of production AI systems and data pipelines.
• Perform exploratory data analysis and develop statistical and machine learning models (e.g., regression, clustering, classification) to address business problems.
• Develop, fine-tune, and optimize machine learning and generative AI models including prompt engineering strategies and structured evaluation frameworks for LLM-based systems to ensure performance, scalability, and reliability.
• Design and build AI-powered product features, including predictive models, optimization systems, and LLM-based applications (e.g., RAG systems, AI assistants, document intelligence).
• Research and apply emerging AI techniques to improve product capabilities and operational efficiency.
• Develop scalable data pipelines, data models and feature engineering workflows.
• Implement MLOps practices including CI/CD, model versioning, monitoring, and automated retraining.
• Design model-serving APIs and ensure system reliability and performance.
• Transition workloads from on-premise infrastructure to AWS and/or Azure environments as needed.
• Design and analyze A/B tests and experiments to validate model and product performance.
• Provide business insights derived from data to guide product and strategic decisions.
• Own technical design of AI solutions in collaboration with Product, Engineering, BI, and Data teams, translating business requirements into scalable system architectures.
• Translate ambiguous business problems into well-defined data and AI solutions with clear success metrics and implementation plans.
• Maintain and enforce data quality, governance, and service standards.
• Identify trends, resolve technical issues, and recommend architectural improvements.
• Contribute to technical documentation for AI systems, infrastructure, and processes.
• Share knowledge through mentorship, technical sessions, and documentation.
• Stay current with advancements in AI, ML infrastructure, and data engineering.
• Travel occasionally to support client workshops, solution design sessions, and technical presentations.
• Other duties as assigned.

• Required Undergraduate or Masters degree in Computer Science, Data Science, Statistics, Engineering or related field.
• Minimum of 3-5 years of designing, building, and operationalizing end-to-end data, ML, NLP and AI systems in production environments.
• Sound knowledge of machine learning lifecycle from data gathering and data…