Data Scientist Lead

FIS•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 turns 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 to deliver measurable business outcomes. It also promotes data science and MLOps best practices, communicates findings to decision-makers, and tracks developments in AI, machine learning, and financial services analytics. 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 the Payments, Banking, or Financial Services industry.
Strong proficiency in Python and SQL.
Hands-on experience with data wrangling, feature engineering, and model development 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 collaborate across product, engineering, and business teams.

Nice To Haves

Experience with big data technologies such as Spark or PySpark.
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 advancements in 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.
Use expertise in data structures and algorithms to analyze and prepare data for modeling, assemble datasets from standard and novel sources, and incorporate them into end-to-end analytical solutions.
Apply machine learning, predictive analytics, natural language processing, and emerging AI techniques to solve 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 to support decision-making.
Stay current on developments in machine learning, AI, Generative AI, and financial services analytics, and bring relevant innovations to the team.