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.

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.   If you are a person with a disability and need a reasonable accommodation to use our search tools and/or apply for a career opportunity review Accessibility at Citi .

View Citi’s EEO Policy Statement and the Know Your Rights poster.