Engineer Lead, Artificial Intelligence / Machine Learning (Data Scientist)

You will be working as a Data Scientist at FIS, where you will be responsible for developing cutting-edge Gen AI and Agentic solutions across the organization. Your key responsibilities will include: - Having 10+ years of overall experience with 4+ years specifically in AI/Gen AI/Agentic AI - Strong experience in statistical analysis, experimentation, and data-driven problem solving - Designing and developing end-to-end ML models such as predictive models, classification and regression models, clustering & segmentation, time-series forecasting, and optimization models - Expertise in feature engineering, data preprocessing, and handling large, messy, real-world datasets - Experience with ML lifecycle including model training, validation, evaluation, monitoring, and retraining - Working with big data ecosystems like Spark, Hadoop, Databricks, EMR, or similar - Operationalizing ML using REST APIs, batch jobs, or cloud AI/ML services - Understanding of MLOps concepts like CI/CD for ML, model versioning, model drift, and monitoring - Experience in cloud environments like AWS, GCP, Azure Furthermore, you should have strong proficiency in Python, including scientific and ML libraries such as pandas, NumPy, scikit-learn, TensorFlow/PyTorch, and Statsmodels. You should also have a strong understanding of statistics, probability, hypothesis testing, and experiment design (A/B tests), hands-on experience with SQL and working with relational & NoSQL databases, and the ability to translate ambiguous business problems into clear analytical solutions. Additionally, you should possess strong data visualization skills using tools like Matplotlib/Seaborn, Plotly, Power BI/Tableau (optional), and experience building reusable analytical pipelines and automation frameworks. Nice to have qualifications include experience with LLM-based analytics, embeddings, or GenAI for data workflows, familiarity with AutoML platforms such as H2O, DataRobot, SageMaker Autopilot, Vertex AI, exposure to Graph ML, NLP, or Computer Vision, knowledge of Reinforcement Learning or causal inference, and strong domain knowledge in BFSI, risk modeling, fraud detection, or customer analytics. Qualifications required for this role are a Bachelors degree in engineering or a related field, or an equivalent combination of education, training, or work experience. FIS, headquartered in Jacksonville, Florida, is a Fortune 500 company and the world's largest global provider dedicated to financial technology solutions. With over 50 years of history in the financial services industry, FIS serves more than 20,000 clients in over 130 countries, processing billions of transactions annually. FIS offers a competitive salary and benefits, a variety of career development tools, resources, and opportunities, and a multifaceted job with a high degree of responsibility and a broad spectrum of opportunities. You will be working as a Data Scientist at FIS, where you will be responsible for developing cutting-edge Gen AI and Agentic solutions across the organization. Your key responsibilities will include: - Having 10+ years of overall experience with 4+ years specifically in AI/Gen AI/Agentic AI - Strong experience in statistical analysis, experimentation, and data-driven problem solving - Designing and developing end-to-end ML models such as predictive models, classification and regression models, clustering & segmentation, time-series forecasting, and optimization models - Expertise in feature engineering, data preprocessing, and handling large, messy, real-world datasets - Experience with ML lifecycle including model training, validation, evaluation, monitoring, and retraining - Working with big data ecosystems like Spark, Hadoop, Databricks, EMR, or similar - Operationalizing ML using REST APIs, batch jobs, or cloud AI/ML services - Understanding of MLOps concepts like CI/CD for ML, model versioning, model drift, and monitoring - Experience in cloud environments like AWS, GCP, Azure Furthermore, you should have strong proficiency in Python, including scientific and ML libraries such as pandas, NumPy, scikit-learn, TensorFlow/PyTorch, and Statsmodels. You should also have a strong understanding of statistics, probability, hypothesis testing, and experiment design (A/B tests), hands-on experience with SQL and working with relational & NoSQL databases, and the ability to translate ambiguous business problems into clear analytical solutions. Additionally, you should possess strong data visualization skills using tools like Matplotlib/Seaborn, Plotly, Power BI/Tableau (optional), and experience building reusable analytical pipelines and automation frameworks. Nice to have qualifications include experience with LLM-based analytics, embeddings, or GenAI for data workflows, familiarity with AutoML platforms such as H2O, DataRobot, SageMaker Autopilot, Vertex AI, exposure to Graph ML, NLP, or Computer Vision, knowledge of Reinforcement Learning or causal inference, and st