Data Scientist Lead

FIS; Jacksonville (TX); On-site; USD 120,000 - 180,000; Full time

Job summary

FIS is actively seeking a data scientist to deploy data-driven analyses and predictive models across Risk, Fraud, Marketing, and Portfolio Management within the financial services domain.

The role focuses on transforming ML results into actionable product recommendations, building end-to-end analytics, and delivering insights to internal and external stakeholders. A strong background in Python, SQL, and BI tools will be essential.

Qualifications

• Master’s degree or higher in Mathematics, Computer Science, Engineering, Operations Research, Statistics, or related quantitative discipline.
• 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; experience with big data technologies such as Spark or PySpark.
• Hands-on data wrangling, feature engineering, and model development using Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, or Plotly.
• Experience deploying machine learning models in production or near-production environments.
• Proficiency with data visualization and business intelligence tools (Tableau or equivalent).
• Strong analytical thinking and problem-solving; ability to translate ambiguous business problems into rigorous analytical frameworks.
• Ability to work collaboratively across product, engineering, and business teams.

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, assembling datasets from standard and novel sources into end-to-end analytical solutions.
• Apply ML, predictive analytics, NLP, and emerging AI techniques to solve problems across payments and financial services.
• Design and execute experiments, hypothesis testing frameworks, and statistical analyses to evaluate strategies and improvements.
• Establish and promote best practices in data science, ML, feature engineering, experimentation, model governance, and MLOps.
• Communicate complex analytical findings through storytelling, dashboards, and executive-ready presentations.
• Stay current on industry trends in ML/AI and financial services analytics; bring innovations to the team.

Skills

Python

SQL

Data wrangling

Feature engineering

Model deployment

Data visualization

Analytical thinking

Education

Master’s degree or higher in Mathematics, Computer Science, Engineering, Operations Research, Statistics

Tools

Spark / PySpark

Pandas

NumPy

Scikit-learn

Matplotlib

Seaborn

Plotly

Tableau

Job description

Job Description

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 role:

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.

What you’ll be doing:

• 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.

What you bring:

• 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 have:

• 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.

What we offer you:

A career at FIS is more than just a job. It’s the chance to shape the future of fintech. At FIS, we offer you:

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

Privacy Statement

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.

EEOC Statement

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.

Sourcing Model

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.

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