Senior Data Scientist

FIS•Atlanta, GA

7d•Hybrid

About The Position

Are you curious, motivated, and forward-thinking? 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. About the Team FIS-Total Issuing Solutions one of the leading credit card processors globally. You will help build production level machine learning models that enhance the value and efficiency of this financial system. As a member of the Data & Analytics team, the data scientist will deploy data-driven exploratory analysis as well as predictive models to solve business problems across the financial services industry, particularly in the area of Risk, Fraud, Marketing, and Portfolio Management. Following the machine learning lifecycle, the data scientist should be able to convert the results into actionable product recommendations to present internally and externally. They will lead Analytics Model development, validation, monitoring, and visualization. Location - Hybrid (3 days in office, 2 days remote): Atlanta, GA

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.

Nice To Haves

Ph.D. in Data Science, Statistics, Mathematics, Computer Science, Engineering, or a related quantitative field.
Experience designing and deploying cloud-native data science and machine learning solutions within AWS environments.
Demonstrated success in productizing machine learning models and analytics solutions for enterprise-scale production environments.
Experience leading the deployment, monitoring, governance, and lifecycle management of production-grade machine learning applications.
Knowledge of Generative AI technologies, including Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, and related frameworks.
Experience mentoring junior data scientists and providing technical leadership across complex analytics initiatives.
Familiarity with modern MLOps practices and model governance within regulated financial services environments.

Responsibilities

Lead the design, development, validation, deployment, and monitoring of advanced analytics, machine learning, and AI solutions that drive measurable business outcomes.
Design and execute experiments, hypothesis testing frameworks, and statistical analyses to evaluate business strategies, product enhancements, and operational improvements.
Analyze and mine large-scale structured and unstructured datasets to uncover actionable insights, identify emerging trends, and support strategic decision-making.
Develop, test, and operationalize analytical and machine learning solutions for both internal stakeholders and external clients, ensuring scalability, reliability, and business impact.
Apply advanced machine learning, predictive analytics, natural language processing (NLP), and emerging AI techniques to solve complex business problems across the payments and financial services ecosystem.
Lead independent quantitative research initiatives, leveraging multiple data sources to generate innovative insights and identify new business opportunities.
Partner with product, engineering, business, and executive stakeholders to translate business objectives into data-driven solutions and measurable outcomes.
Communicate complex analytical findings through compelling storytelling, executive-ready presentations, dashboards, and visualizations that drive informed decision-making.
Design and develop automated dashboards, performance scorecards, and self-service analytics solutions to monitor key business metrics, customer behaviors, model performance, and operational health.
Establish and promote best practices in data science, machine learning, experimentation, model governance, and MLOps throughout the organization.
Lead proof-of-concept (POC) initiatives to evaluate emerging technologies, machine learning techniques, and Generative AI capabilities, translating successful pilots into production-ready solutions.
Drive model lifecycle management, including feature engineering, model training, validation, deployment, monitoring, retraining, and performance optimization.
Mentor and develop junior data scientists, fostering a culture of technical excellence, innovation, collaboration, and continuous learning.
Provide technical leadership and guidance on analytical methodologies, model selection, data quality, and solution architecture.
Collaborate with data engineering teams to define data requirements, optimize data pipelines, and ensure availability of high-quality data for analytics and machine learning initiatives.
Ensure adherence to regulatory, security, compliance, and model governance standards within a highly regulated financial services environment.
Stay current on industry trends and advancements in machine learning, artificial intelligence, Generative AI, cloud technologies, and financial services analytics.
Contribute to strategic planning by identifying opportunities where advanced analytics and AI can create competitive advantage and business value.
Perform other duties and responsibilities as assigned.