Data Scientist Lead

FIS Global•Atlanta, GA

About The Position

As a key member of FIS’s Data Science team, the Data Scientist Lead uses data-driven exploratory analysis, predictive models, and AI solutions to address business problems across financial services, particularly in Risk, Fraud, Marketing, and Portfolio Management. The role follows the machine learning lifecycle and translates analytical results into actionable product recommendations for internal and external audiences. The position leads the design, development, validation, deployment, and monitoring of advanced analytics, machine learning, and AI solutions. It applies statistical and machine learning methods to business challenges across payments and financial services, establishes data science and MLOps best practices, and communicates findings to support informed decision-making. Current and future sponsorship are not available for this position.

Requirements

Master's degree or higher in Mathematics, Computer Science, Engineering, Operations Research, Statistics, or a related quantitative discipline.
At least 5 years of experience developing and deploying end-to-end machine learning, predictive analytics, and data science solutions within Payments, Banking, or Financial Services.
Strong proficiency in Python and SQL.
Hands-on data wrangling, feature engineering, and model development experience using libraries such as Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, or Plotly.
Demonstrated experience building and deploying machine learning models in a production or near-production environment.
Proficiency with data visualization and business intelligence tools such as Tableau or equivalent.
Strong analytical thinking and problem-solving skills, including the ability to translate ambiguous business problems into rigorous analytical frameworks.
Ability to work collaboratively across product, engineering, and business teams.

Nice To Haves

Experience with big data technologies such as Spark or PySpark.
Experience within the Payments, Banking, or Financial Services industry.
Hands-on experience with Databricks, including MLflow, Model Registry, collaborative notebooks, and MLOps workflows.
Experience deploying cloud-native machine learning solutions, particularly in AWS environments.
Working familiarity with Transformer Models and Agentic AI technologies.
Knowledge of model governance, regulatory compliance, and MLOps best practices in 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.
Analyze and prepare data for modeling using expertise in data structures and algorithms; assemble datasets from standard and novel data sources for end-to-end analytical solutions.
Apply machine learning, predictive analytics, natural language processing, and emerging AI techniques to complex problems across payments and financial services.
Design and execute experiments, hypothesis-testing frameworks, and statistical analyses to evaluate business strategies, product enhancements, and operational improvements.
Establish and promote best practices in data science, machine learning, feature engineering, experimentation, model governance, and MLOps.
Communicate analytical findings through storytelling, executive-ready presentations, dashboards, visualizations, and self-service analytics tools.
Stay current on machine learning, AI, Generative AI, and financial services analytics trends and bring relevant innovations to the team.