Lead Data Scientist – ML & AI Solutions

Fidelity Information Services, LLC; Atlanta (GA); On-site; USD 120,000 - 170,000; Full time

Benefits offered by this job

Competitive salary and benefits

Always-on learning and development

Collaborative work environment

Opportunities to give back

Voice in the future of fintech

Job summary

Fidelity Information Services, LLC (FIS) seeks a data scientist to deploy data-driven exploratory analyses and predictive models across risk, fraud, marketing, and portfolio management in financial services.

The role emphasizes end-to-end ML lifecycle, dataset construction from diverse sources, and translating results into actionable product recommendations for internal and external stakeholders, with a focus on governance and MLOps best practices.

Qualifications

• Master's degree or higher in a quantitative field.
• 5+ years of experience in end-to-end ML/DS in Payments/Financial Services.
• Strong proficiency in Python and SQL; Spark experience is a plus.

Responsibilities

• Lead design, development, validation, deployment, and monitoring of analytics and AI solutions.
• Prepare data from diverse sources for modeling.
• Apply ML, NLP, GenAI to payments/financial services problems.
• Design experiments and statistical analyses to evaluate strategies.
• Promote best practices in data science and MLOps.

Skills

Python

SQL

Spark

Pandas

NumPy

Scikit-learn

Data wrangling

Feature engineering

Data visualization

Tableau

ML deployment

Collaboration across teams

Education

Master's degree or higher in Mathematics/CS/Engineering/OR/Statistics

Tools

Spark

PySpark

Databricks

MLflow

Model Registry

Job description

Fidelity Information Services, LLC (FIS) seeks a data scientist to deploy data-driven exploratory analyses and predictive models across risk, fraud, marketing, and portfolio management in financial services.

The role emphasizes end-to-end ML lifecycle, dataset construction from diverse sources, and translating results into actionable product recommendations for internal and external stakeholders, with a focus on governance and MLOps best practices.