Data Scientist, AI/ML & Production Pipelines

REPAY Company; Atlanta, Northern (GA, KY); Hybrid; USD 120,000 - 170,000; Full time

Benefits offered by this job

100% health coverage for employee

401(k) employer match

Employee Stock Purchase Plan

Annual bonus program

Job summary

REPAY, headquartered in Atlanta, is seeking a Data Scientist to design, build, and operationalize intelligent AI systems powering ML features and product capabilities across platforms. This role partners with Product, BI, and Data Engineering to set standards, architect scalable AI systems, and drive measurable business impact.

You will develop end-to-end data-to-AI pipelines, apply NLP and generative AI techniques, implement MLOps, and help transition workloads to cloud environments.

Qualifications

• Undergraduate or Masters degree in CS, Data Science, Statistics, Engineering or related field.

Responsibilities

• Deliver actionable, data-driven insights and reporting to inform decisions across products and AI systems.
• Design and maintain scalable data-to-AI pipelines covering ingestion, transformation, feature engineering, model training, deployment and monitoring.
• Develop AI-powered product features and ML/AI models with evaluation frameworks for LLM-based systems.
• Own and evolve AI solutions in collaboration with Product, Engineering, BI, and Data teams.

Skills

Python

SQL

LLM-based systems

Data science lifecycle

CI/CD and MLOps

Education

Bachelor's degree in CS/DS/Statistics/Engineering

Master's degree in CS/DS/Statistics/Engineering

Tools

Databricks

AWS Bedrock

AWS SageMaker

Cloud AI/ML services

Job description

REPAY, headquartered in Atlanta, is seeking a Data Scientist to design, build, and operationalize intelligent AI systems powering ML features and product capabilities across platforms. This role partners with Product, BI, and Data Engineering to set standards, architect scalable AI systems, and drive measurable business impact.

You will develop end-to-end data-to-AI pipelines, apply NLP and generative AI techniques, implement MLOps, and help transition workloads to cloud environments.