Data Scientist Sr Lead
Hybrid (3 days in office, 2 days remote): Atlanta, GA, Columbus, GAAll locationsHybrid (3 days in office, 2 days remote): Atlanta, GA, Columbus, GAJacksonville, FLAtlanta, GAColumbus, GAUS GA ATL 201 STE 900US FL JAX 347 Hybrid
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About the role
At this time, we are unable to offer visa sponsorship for this position. Candidates must be legally authorized to work for any employer in the United States (or applicable country) on a full-time basis without the need for current or future immigration sponsorship. 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. FIS-Total Issuing Solutions one of the leading credit card processors globally. You will help build production level machine learning models that enhance the value and efficiency of this financial system. As a member of the Data & Analytics team, the data scientist will deploy data-driven exploratory analysis as well as predictive models to solve business problems across the financial services industry, particularly in the area of 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. They will lead Analytics Model development, validation, monitoring, and visualization. Location - Hybrid (3 days in office, 2 days remote): Atlanta, GA, Columbus, GA or Jacksonville, FL
What you'll bring
- Master’s degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, Economics, or another 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 data science programming languages and big data technologies, including Python, SQL, Spark, PySpark, R, and Hadoop.
- Extensive experience with data wrangling, feature engineering, and model development using libraries such as Pandas, NumPy, Scikit-learn, Plotly, Matplotlib, and Seaborn.
- Advanced expertise in data visualization and business intelligence platforms, including Tableau.
- Hands-on experience with the Databricks platform, including MLflow, AutoML, Model Registry, collaborative notebooks, and MLOps workflows.
- Demonstrated ability to identify innovative business opportunities, develop proof-of-concepts (POCs), and translate successful pilots into scalable solutions.
- Strong experience building and deploying machine learning models, including classification, clustering, and predictive models such as Random Forest, XGBoost, Gradient Boosting, and K-Means.
- Experience applying Natural Language Processing (NLP) techniques to solve business challenges.
- Proven ability to communicate complex analytical concepts and insights to both technical and non-technical stakeholders.
- Ph.D. in Data Science, Statistics, Mathematics, Computer Science, Engineering, or a related quantitative field.
- Experience designing and deploying cloud-native data science and machine learning solutions within AWS environments.