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About the Role

FIS-Total Issuing Solutions is a leading credit card processor globally. This role will contribute to building production-level machine learning models to enhance the value and efficiency of this financial system. The Data Scientist will deploy data-driven exploratory analysis and predictive models to solve business problems in Risk, Fraud, Marketing, and Portfolio Management, converting results into actionable product recommendations.

Responsibilities

Lead the design, development, validation, deployment, and monitoring of advanced analytics, machine learning, and AI solutions.
Design and execute experiments, hypothesis testing frameworks, and statistical analyses.
Analyze and mine large-scale structured and unstructured datasets to uncover actionable insights and identify emerging trends.
Develop, test, and operationalize analytical and machine learning solutions for scalability, reliability, and business impact.
Apply advanced machine learning, predictive analytics, NLP, and AI techniques to solve complex business problems.
Lead independent quantitative research initiatives to generate innovative insights.
Partner with stakeholders to translate business objectives into data-driven solutions.
Communicate complex analytical findings through presentations, dashboards, and visualizations.
Design and develop automated dashboards, performance scorecards, and self-service analytics solutions.
Establish and promote best practices in data science, machine learning, experimentation, model governance, and MLOps.
Lead proof-of-concept (POC) initiatives to evaluate emerging technologies and Generative AI capabilities.
Drive model lifecycle management, including feature engineering, training, validation, deployment, monitoring, and optimization.
Mentor and develop junior data scientists, fostering a culture of technical excellence and innovation.
Provide technical leadership and guidance on analytical methodologies, model selection, data quality, and solution architecture.
Collaborate with data engineering teams to define data requirements and optimize data pipelines.
Ensure adherence to regulatory, security, compliance, and model governance standards.
Stay current on industry trends and advancements in machine learning, AI, Generative AI, cloud technologies, and financial services analytics.
Contribute to strategic planning by identifying opportunities for advanced analytics and AI.
Perform other duties and responsibilities as assigned.
Mentor and coach junior data scientists, fostering a culture of continuous learning.
Provide constructive feedback through regular code reviews and design critiques.
Identify skill gaps within the team and develop training initiatives.
Own the end-to-end delivery of complex predictive and prescriptive analytics initiatives.
Translate ambiguous business problems into rigorous analytical frameworks.
Drive innovation by researching new algorithms, tools, and methodologies.
Partner with product, engineering, and business stakeholders to align data science initiatives with organizational goals.

Requirements

Master’s degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, Economics, or another quantitative discipline.
5+ years of experience developing and deploying end-to-end machine learning, predictive analytics, and data science solutions within the Payments, Banking, or Financial Services industry.
Strong proficiency in data science programming languages and big data technologies, including Python, SQL, Spark, PySpark, R, and Hadoop.
Extensive experience with data wrangling, feature engineering, and model development using libraries such as Pandas, NumPy, Scikit-learn, Plotly, Matplotlib, and Seaborn.
Advanced expertise in data visualization and business intelligence platforms, including Tableau.
Hands-on experience with the Databricks platform, including MLflow, AutoML, Model Registry, collaborative notebooks, and MLOps workflows.
Demonstrated ability to identify innovative business opportunities, develop proof-of-concepts (POCs), and translate successful pilots into scalable solutions.
Strong experience building and deploying machine learning models, including classification, clustering, and predictive models such as Random Forest, XGBoost, Gradient Boosting, and K-Means.
Experience applying Natural Language Processing (NLP) techniques to solve business challenges.
Proven ability to communicate complex analytical concepts and insights to both technical and non-technical stakeholders.
Ph.D. in Data Science, Statistics, Mathematics, Computer Science, Engineering, or a related quantitative field (Preferred).
Experience designing and deploying cloud-native data science and machine learning solutions within AWS environments (Preferred).
Demonstrated success in productizing machine learning models and analytics solutions for enterprise-scale production environments (Preferred).
Experience leading the deployment, monitoring, governance, and lifecycle management of production-grade machine learning applications (Preferred).
Knowledge of Generative AI technologies, including Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, and related frameworks (Preferred).
Experience mentoring junior data scientists and providing technical leadership across complex analytics initiatives (Preferred).
Familiarity with modern MLOps practices and model governance within regulated financial services environments (Preferred).
Candidates must be legally authorized to work for any employer in the United States (or applicable country) on a full-time basis without the need for current or future immigration sponsorship.

Skills

Python
SQL
Spark
PySpark
R
Hadoop
Pandas
NumPy
Scikit-learn
Plotly
Matplotlib
Seaborn
Tableau
Databricks
MLflow
AutoML
Model Registry
MLOps
Random Forest
XGBoost
Gradient Boosting
K-Means
Natural Language Processing (NLP)
Generative AI
Large Language Models (LLMs)
Retrieval-Augmented Generation (RAG)
AI agents
AWS

Location

Atlanta, GA
Columbus, GA
Jacksonville, FL

Work Type

Full time
Hybrid (3 days in office, 2 days remote)

Experience Level

Experienced (relevant combo of work and education)

Education Level

Master’s degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, Economics, or another quantitative discipline.
Ph.D. in Data Science, Statistics, Mathematics, Computer Science, Engineering, or a related quantitative field (Preferred).

Benefits

A voice in the future of fintech
Always-on learning and development
Collaborative work environment
Opportunities to give back
Competitive salary and benefits

About the Company

At FIS you’ll have the opportunity to work on some of the most challenging and relevant issues in financial services and technology.
Our talented people empower us, and we believe in being part of a team that is open, collaborative, entrepreneurial, passionate and above all fun.
FIS is committed to protecting the privacy and security of all personal information that we process in order to provide services to our clients.
For specific information on how FIS protects personal information online, please see the Online Privacy Notice.
Recruitment at FIS works primarily on a direct sourcing model; a relatively small portion of our hiring is through recruitment agencies.
FIS does not accept resumes from recruitment agencies which are not on the preferred supplier list and is not responsible for any related fees for resumes submitted to job postings, our employees, or any other part of our company.

Equal Opportunity

FIS is an equal opportunity employer. We evaluate qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, marital status, genetic information, national origin, disability, veteran status, and other protected characteristics.
The EEO is the Law poster is available here supplement document available here
For positions located in the US, the following conditions apply. If you are made a conditional offer of employment, you will be required to undergo a drug test.
ADA Disclaimer: In developing this job description care was taken to include all competencies needed to successfully perform in this position. However, for Americans with Disabilities Act (ADA) purposes, the essential functions of the job may or may not have been described for purposes of ADA reasonable accommodation. All reasonable accommodation requests will be reviewed and evaluated on a case-by-case basis.