Job description

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. ABOUT THE ROLE 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. ROLES & 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. QUALIFICATIONS 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 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. WHY JOIN REPAY.… BECAUSE CULTURE IS EVERYTHING GROWTH & PEOPLE-CENTERED LEADERSHIP As the industry-leading financial technology provider in the Consumer Finance and Business to Business spaces, we continue to set the standard for application development and delivery. In 2019, REPAY became a public company listed on the Nasdaq Stock Market (RPAY). For the past three consecutive years, we have placed on the ACG® Atlanta Georgia Fast 40, a list recognizing the top 40 fastest-growing middle-market companies in Georgia. REPAY’s leadership empowers each team member to make a difference and stretch to their fullest potential. Our dedication to frequent, transparent communication is shown with companywide meetings where our leaders share company vision and encourage employees to ask questions. FUN WORK ENVIRONMENT & GREAT TEAMS We offer it all: business to casual dress, great snacks & beverages, and open-air collaborative team settings. REPAY has been certified as a Great Place to Work® company for 2017, 2018, 2019, 2020, 2021, and 2022. The REPAY team is fun, smart, collaborative, and truly enjoys working together. Making a difference in our local communities – we support several philanthropic initiatives every year to give back to our local communities. We are self-driven, motivated professionals who do not require micro-management to ensure we produce high quality and timely work. INNOVATION & EDUCATION We create highly sophisticated payment processing applications and are always pushing the boundaries of what is possible. We are constantly revolutionizing the industry by building on new ideas from clients and employees. We provide the resources necessary to ensure new innovations can develop quickly and with quality. We encourage continuing education, including professional conferences and events. PUTTING OUR PEOPLE FIRST We believe our people are the best, and we care immensely about their success. We offer a comprehensive benefits package which includes 100% coverage of employee healthcare premiums and several free benefits, including life insurance, disability insurance, and work-life balance resources. All benefits go into effect day one. Our employees’ futures are important to us, which is why we have a 401(k)-employer match and and an Employee Stock Purchase Plan. REPAY employees are eligible to participate in our Annual Bonus Program. This bonus award reflects excellent performance of individual contributions and goals achieved during the past year. REPAY’s core values are Excellence, Passion, Innovation, Respect, and Integrity. REPAY is an Equal Opportunity Employer and we promote a company culture where diversity, equity and inclusion are central. We are committed to build our teams and grow a company in which employees can succeed, regardless of race, color, national origin, sex, sexual orientation, gender identity or expression, transgender status, pregnancy, religion, age (40 and over), disability, service in the uniformed services, protected veteran status, genetic information, or any other classification protected by federal, state or local law. Celebrating our diverse backgrounds, views and beliefs allows us to embrace what makes us unique and continue to innovate and push the boundaries of what is possible. 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.

Requirements

Must-haves

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.
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.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

mlaillmmachine learningci/cdawsazurepythonsqlapigo

Platform: workday
Effort: long

Benefits

Benefits detected in the listing.

Vision

401(K)