Lead I- Data Science FS

Senior Data Scientist Role Summary We are seeking a highly skilled Senior Data Scientist with expertise in Machine Learning, Artificial Intelligence, Large Language Models (LLMs), foundation models, and fine-tuning techniques. In this role, you will architect, build, and deploy advanced ML and AI solutions that power next-generation products across payments, fraud detection, risk decisioning, and financial data enrichment. This position is ideal for someone who is passionate about building enterprise-grade AI and ML applications and applying cutting-edge techniques to real-world financial and payments challenges. Key Responsibilities Design, build, and optimize Generative AI, LLM, and multimodal foundation models for enterprise fintech applications. Fine-tune or adapt open-source and proprietary models. Build high-performance models for NLP, document intelligence, anomaly detection, risk scoring, predictive analytics, and decisioning use cases. Lead experimentation to evaluate model accuracy, scalability, and fairness. Partner with engineering teams to deploy models on cloud-based ML pipelines (Azure, AWS) and data platforms (Databricks and Snowflake). Work with large-scale structured and unstructured datasets across the payments and financial ecosystem. Implement model monitoring, drift detection, and continuous retraining strategies. Evaluate and operationalize new AI technologies, foundation model architectures, responsible AI frameworks, and emerging research. Drive POCs and innovation initiatives that enhance AI capabilities and differentiate products and services. Required Qualifications Master's or PhD in Computer Science, Data Science, Machine Learning, Artificial Intelligence, or a related field. 5+ years of hands-on experience building and deploying machine learning models in production environments. Proven expertise with LLMs, transformer architectures, transfer learning, and model fine-tuning. Strong proficiency in Python, PyTorch or TensorFlow, and ML libraries such as Hugging Face Transformers. Experience with cloud ML platforms, containerization technologies (Docker/Kubernetes), and MLOps tools. Solid understanding of statistical modeling, optimization techniques, and model evaluation methodologies. Strong communication skills and ability to collaborate effectively in cross-functional, fast-paced environments. Preferred Qualifications Experience working in fintech, payments, banking, fraud detection, or risk management domains. Background in vector databases, Retrieval-Augmented Generation (RAG) pipelines, and knowledge graph integration. Experience with data privacy, model governance, and Responsible AI frameworks. Contributions to open-source AI/ML communities or published research.