Data Scientist
Atlanta, Georgia, United States of America
- Pay
- Pay not listed
- Work setting
- Not stated
- Employment
Full-time — employment source
Position Type : Full time Type Of Hire : Experienced (relevant combo of work and education)
Read the full posting
What you’ll work on
Full postingAs 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.
Participate in the design, development, validation, deployment, and monitoring of advanced analytics, machine learning, and AI solutions that drive measurable business outcomes.
Design, build, validate, deploy and monitor machine learning and AI solutions
Assemble and prepare datasets from standard and novel sources for modeling
Design and run experiments and statistical tests to evaluate business strategies
Set best practices for feature engineering, model governance and MLOps
From the employer’s posting
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.
Participate in the design, development, validation, deployment, and monitoring of advanced analytics, machine learning, and AI solutions that drive measurable business outcomes.
Participate in the design, development, validation, deployment, and monitoring of advanced analytics, machine learning, and AI solutions
assembling datasets from both standard and novel data sources
Design and execute experiments, hypothesis testing frameworks, and statistical analyses
Establish and promote best practices in data science, machine learning, feature engineering, experimentation, model governance, and MLOps
What you’ll bring
All qualificationsCore experience
- Master's degree or higher in Mathematics, Computer Science, Engineering, Operations Research, Statistics, or a related quantitative discipline.
- Experience within the Payments, Banking, or Financial Services industry.
- 1-3 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.
- Hands-on experience with the Databricks platform, including MLflow, Model Registry, collaborative notebooks, and MLOps workflows.
- Hands-on experience with data wrangling, feature engineering, and model development using libraries such as Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, or Plotly.
- Experience deploying cloud-native machine learning solutions, particularly within AWS environments.
Preferred experience
- Strong proficiency in Python and SQL; experience with big data technologies such as Spark, PySpark, a plus.
Qualification wording
Master's degree or higher in Mathematics, Computer Science, Engineering, Operations Research, Statistics, or a related quantitative discipline.
Experience within the Payments, Banking, or Financial Services industry.
1-3 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.
Hands-on experience with the Databricks platform, including MLflow, Model Registry, collaborative notebooks, and MLOps workflows.
Hands-on experience with data wrangling, feature engineering, and model development using libraries such as Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, or Plotly.
Experience deploying cloud-native machine learning solutions, particularly within AWS environments.
Strong proficiency in Python and SQL; experience with big data technologies such as Spark, PySpark, a plus.
Tools in this posting
- Python
- SQL
- AWS
- Databricks
- MLflow
- Spark
- Tableau
- Matplotlib
- NumPy
- pandas
- Plotly
- PySpark
- scikit-learn
Source — Tool mentions in context
- 1-3 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.
- 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. - Familiarity with emerging advancements in Transformer Models and Agentic AI technologies.
- 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.
- 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.
- 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.
Posting record
Report an error- Status in our records
- Live
- First seen by us
- Aug 26, 2026 · 57 days ago
- Recorded sightings
- 33
- Last seen by us
- Oct 23, 2026
These dates show when we found the listing. Check the employer’s website to confirm it is still accepting applications.
Your next step
- Have your resume and examples of relevant work ready.
- Check the listed location, eligibility and core experience before starting.
- Ask the employer about the pay range before committing time to the process.
Complete your application on fis.wd5.myworkdayjobs.com. The employer’s form will show what is required.
Already applied? Track this application
Job description
Position Type :
Full timeType Of Hire :
Experienced (relevant combo of work and education)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:
- Participate in 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.
- 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.
- 1-3 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.
- 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.
#pridepass
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
No pay amount identified in the saved description.
- Location & working pattern
Atlanta, Georgia, United States of America
Working pattern and location restrictions need checking in the full posting.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.
See how this role fits your experience
Add your resume to compare the role’s scope, tools and requirements with your experience.