FIS
Financial Services
SeniorDataScientist
Neural analysis suggests this role is
optimal for Senior candidates.
“Senior Data Scientist at FIS. Skills: Machine Learning, Data Science, Predictive Analytics, AI. Lead design, development, validation, deployment, and monitoring of. Design and execute experiments, hypothesis testing frameworks, and”
What You'll Achieve.
Enhance value and efficiency of financial system; Solve business problems; Drive measurable business outcomes; Support strategic decision-making; Ensure scalability, reliability, and business impact; Generate innovative insights; Identify new business opportunities; Translate business objectives into data-driven solutions; Drive informed decision-making; Monitor key business metrics; Create competitive advantage and business value
Industry & Context.
Data-driven exploratory analysis; Predictive models; Actionable insights; Complex business problems
What They're Looking For.
Must Have
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, Proficiency in 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, 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
Nice to Have
Ph. D. in Data Science, Statistics, Mathematics, Computer Science, or a related quantitative field, Experience designing and deploying cloud-native data science and machine learning solutions within AWS environments, Demonstrated success in productizing machine learning models and analytics solutions for enterprise-scale production environments, Experience leading the deployment, monitoring, governance, and lifecycle management of production-grade machine learning applications, Knowledge of Generative AI technologies, including Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, and related frameworks, Experience mentoring junior data scientists and providing technical leadership across complex analytics initiatives, Familiarity with modern MLOps practices and model governance within regulated financial services environments
What You'll Do.
Design and execute experiments
hypothesis testing frameworks
Analyze and mine large-scale structured and unstructured datasets
and operationalize analytical and machine learning
Apply advanced machine learning
Lead independent quantitative research initiatives
and executive stakeholders
Communicate complex analytical findings through compelling storytelling
Design and develop automated dashboards
performance scorecards
Establish and promote best practices in data science
Lead proof-of-concept (POC) initiatives to evaluate emerging technologies
Drive model lifecycle management
including feature engineering
Mentor and develop junior data scientists
Provide technical leadership and guidance on analytical methodologies
Collaborate with data engineering teams to define data
Ensure adherence to regulatory
Stay current on industry trends and advancements in
Contribute to strategic planning by identifying opportunities where
Perform other duties and responsibilities as assigned
How You'll Work.
Team & Collaboration
Product, engineering, business, and executive stakeholders; Data engineering teams
Communication Scope
Executive presentations; Storytelling; Dashboards; Visualizations
Full 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 Team** 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 ** **What you will be doing** * Lead the design, development, validation, deployment, and monitoring of advanced analytics, machine learning, and AI solutions that drive measurable business outcomes. * Design and execute experiments, hypothesis testing frameworks, and statistical analyses to evaluate business strategies, product enhancements, and operational improvements. * Analyze and mine large-scale structured and unstructured datasets to uncover actionable insights, identify emerging trends, and support strategic decision-making. * Develop, test, and operationalize analytical and machine learning solutions for both internal stakeholders and external clients, ensuring scalability, reliability, and business impact. * Apply advanced machine learning, predictive analytics, natural language processing
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