Lead AI/ML Data Scientist- Vice president

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.

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