Lead AI/ML Data Scientist- Vice president

• 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