Data Scientist

Summary

REPAY is a financial technology and payment processing company that provides electronic payment and funding solutions. The Data Scientist designs, builds, and operationalizes data, machine learning, and AI systems, including generative AI and LLM-based applications, while partnering with Product, BI, and Data Engineering teams to deliver measurable business impact.

Responsibilities

• 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

Skills

• 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 preparation to feature engg to model deployment, monitoring and iteration
• Strong programming skills in Python & SQL, experience writing production-quality code
• Hands-on experience with LLM-based systems (e.g., RAG architectures, prompt engineering, model evaluation, API integration)
• Exposure to Databricks, AWS Bedrock, AWS Sagemaker, Cloud based AI/ML services and modern data platforms
• Experience integrating external AI/LLM APIs and building and operationalizing application-layer AI services is desirable
• Strong software engineering fundamentals, including experience with CI/CD, testing practices, and containerization concepts
• Strong analytical and problem-solving skills, with the ability to translate ambiguous business requirements into structured AI and data solutions
• Excellent written and verbal communication skills, with the ability to explain complex technical concepts to non-technical stakeholders
• Ability to collaborate effectively across Product, Engineering, BI, and Data teams
• Strong organizational skills and ability to manage multiple priorities in a fast-paced environment
• Proactive, ownership-oriented mindset with the ability to work independently and drive solutions from concept to production
• Professionalism and composure when operating under pressure or resolving production issues
• We are interested in every qualified candidate who is eligible to work in the United States
• This position is not eligible for hire in California
• Additionally, we are not able to sponsor visas

Qualifications

Must Haves

• 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 preparation to feature engg to model deployment, monitoring and iteration
• Strong programming skills in Python & SQL, experience writing production-quality code
• Hands-on experience with LLM-based systems (e.g., RAG architectures, prompt engineering, model evaluation, API integration)
• Exposure to Databricks, AWS Bedrock, AWS Sagemaker, Cloud based AI/ML services and modern data platforms
• Experience integrating external AI/LLM APIs and building and operationalizing application-layer AI services is desirable
• Strong software engineering fundamentals, including experience with CI/CD, testing practices, and containerization concepts
• Strong analytical and problem-solving skills, with the ability to translate ambiguous business requirements into structured AI and data solutions
• Excellent written and verbal communication skills, with the ability to explain complex technical concepts to non-technical stakeholders
• Ability to collaborate effectively across Product, Engineering, BI, and Data teams
• Strong organizational skills and ability to manage multiple priorities in a fast-paced environment
• Proactive, ownership-oriented mindset with the ability to work independently and drive solutions from concept to production
• Professionalism and composure when operating under pressure or resolving production issues
• We are interested in every qualified candidate who is eligible to work in the United States
• This position is not eligible for hire in California
• Additionally, we are not able to sponsor visas

Benefits

• Business-casual dress
• Great snacks & beverages
• Open-air collaborative team settings
• Resources necessary to ensure new innovations can develop quickly and with quality
• Continuing education, including professional conferences and events
• 100% coverage of employee healthcare premiums
• Free life insurance
• Free disability insurance
• Work-life balance resources
• All benefits go into effect day one
• 401(k)-employer match
• Employee Stock Purchase Plan
• Eligibility to participate in the Annual Bonus Program