Lead AI-ML

About the Team: Citi is looking for a Lead AI/ML Data Scientist to join the Olympus Data Reconciliation and Engineering team, where you will shape the next generation of AI and machine learning capabilities powering enterprise-scale reconciliation across global processing hubs. In this role, you will drive the full lifecycle of ML model development — from ideation and architecture through to deployment and adoption — delivering measurable impact across Capital Markets operations, risk, and finance. Your work will sit at the intersection of advanced data science and real-world financial systems, influencing outcomes at a global scale. Responsibilities: * Design, build, and deploy AI and machine learning models — including Agentic AI and Generative AI solutions — to solve complex reconciliation and data engineering challenges at enterprise scale. * Lead the end-to-end ML model development lifecycle, from requirements gathering and data preprocessing through to ensemble modeling, validation, and production integration. * Analyze large volumes of structured and unstructured financial data to uncover trends, patterns, and opportunities for optimization across banking platforms. * Define and deliver ML model roadmaps in collaboration with technical and business teams, ensuring alignment with project timelines, budgets, and Citi's architecture standards. * Translate complex data findings into clear visualizations and strategic recommendations that inform decisions made by senior business and technology leaders. * Partner with engineering, operations, and cross-functional teams to ensure seamless model integration, long-term scalability, and reliable performance in production environments. * Identify and communicate technology risks and their business implications, developing mitigation strategies and maintaining transparency with stakeholders at all levels. * Maintain comprehensive model documentation and support knowledge transfer to ensure continuity and adoption across teams. Required Qualifications & Skills: Technical Expertise: * 10+ years hands-on experience in AI/ML development and big data engineering within Financial Services, Insurance, or Telecom environments * Expert-level proficiency in Python (scikit-learn, TensorFlow, PyTorch, Pandas, NumPy), R (caret, tidyverse, mlr3), and SQL (PostgreSQL, Oracle, MySQL) * Deep technical knowledge implementing supervised and unsupervised ML algorithms: linear/logistic regression, neural networks (CNN, RNN, LSTM, Transformers), k-means clustering, DBSCAN, decision trees (CART, C4.5), and ensemble methods (Random Forest, XGBoost, LightGBM, CatBoost) * Proven experience building and deploying Agentic AI and LLM-based solutions using: * LangGraph for complex agent orchestration and state management * LangChain for chain-of-thought reasoning and retrieval-augmented generation (RAG) * Agent Development Kit (ADK) for enterprise-grade autonomous agent development * Production-level experience with MLOps frameworks and infrastructure: * Apache Airflow for ML pipeline orchestration and workflow automation * Kubernetes for containerized model deployment and scaling * Docker for reproducible ML environments * Advanced proficiency with distributed computing technologies: * Apache Spark (PySpark, Spark MLlib) for large-scale data processing * Hadoop ecosystem (HDFS, MapReduce, YARN) * Apache Hive for data warehousing and SQL-on-Hadoop * Expertise with cloud-native data platforms: * AWS S3 for scalable data lake storage * Amazon Redshift for enterprise data warehousing * AWS SageMaker, Azure ML, or Google Vertex AI (beneficial) * Strong background in data reconciliation frameworks, data quality validation, and ETL/ELT pipelines for financial data processing at enterprise scale Beneficial Skills & Qualifications: * Hands-on experience with advanced statistical modeling: Generalized Linear Models (GLM), Random Forest, Gradient Boosting (AdaBoost, XGBoost), and Natural Language Processing (NLP) techniques including text mining, topic modeling (LDA), and sentiment analysis * Experience with model versioning and experiment tracking tools (Mlflow, Weights & Biases, DVC) * Proficiency with Git/GitHub/Bitbucket for version control and collaborative development * Knowledge of CI/CD pipelines for ML model deployment (Jenkins, GitLab CI, GitHub Actions) * Familiarity with data visualization libraries (Matplotlib, Seaborn, Plotly) and BI tools (Tableau, Power BI) * Experience with real-time streaming data frameworks (Kafka, Kinesis) * Passion for staying current with emerging AI/ML frameworks, research papers, and open-source contributions Education: Bachelor’s or Master’s degree in Computer Science, Data Science, Software Engineering, Information Systems, Mathematics, Statistics or related fields of study. \------------------------------------------------------ ## Job Family Group: Technology \------------------------------------------------------ ## Job Family: Data Science \------------------------------------------------------ ## Time Type: Full time \------------------------------------------------------ ## Most Relevant Skills Please see the requirements listed above. \------------------------------------------------------ ## Other Relevant Skills For complementary skills, please see above and/or contact the recruiter. \------------------------------------------------------ Citi is an equal opportunity employer, and qualified candidates will receive consideration without regard to their race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other characteristic protected by law. 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