Data Scientist Lead

FIS Global•Atlanta, GA

About The Position

As a key member of the Data Science team, the data scientist will deploy data-driven exploratory analysis as well as predictive models and AI solutions to solve business problems across the financial services industry, particularly in 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.

Requirements

Master's degree or higher in Mathematics, Computer Science, Engineering, Operations Research, Statistics, or a related 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 Python and SQL; experience with big data technologies such as Spark, PySpark, a plus.
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 (e.g., Tableau or equivalent).
Strong analytical thinking and problem-solving skills; ability to translate ambiguous business problems into rigorous analytical frameworks.
Ability to work collaboratively across product, engineering, and business teams.

Nice To Haves

Experience within the Payments, Banking, or Financial Services industry.
Hands-on experience with the Databricks platform, including MLflow, Model Registry, collaborative notebooks, and MLOps workflows.
Experience deploying cloud-native machine learning solutions, particularly within AWS environments.
Working familiarity with emerging advancements in Transformer Models and Agentic AI technologies.
Knowledge of model governance, regulatory compliance, and MLOps best practices 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.
Leverage expertise in data structures and algorithms to analyze and prepare data for modeling, assembling datasets from both standard and novel data sources and incorporate them into end-to-end analytical solutions.
Apply advanced machine learning, predictive analytics, natural language processing (NLP), and emerging AI techniques (GenAI, Agentic etc.) to solve complex business problems across the payments and financial services ecosystem.
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 throughout the organization.
Communicate complex analytical findings through compelling storytelling, executive-ready presentations, dashboards, visualizations and self-service analytics tools. that drive informed decision-making.
Stay current on industry trends in machine learning, AI, Generative AI, and financial services analytics; bring relevant innovations to the team.