Lead AI/ML Data Scientist - VP

Citibank (Switzerland) AG; Mississauga; On-site; Confidential; Full time

Job summary

Citi is seeking a Lead AI/ML Data Scientist to join the Olympus Data Reconciliation and Engineering team. You will own the end‑to‑end lifecycle of ML model development and deployment across global processing hubs, shaping next‑gen AI capabilities powering enterprise reconciliation, risk, and finance.

You will design, implement, and operationalize models using Agentic AI and LLM‑based approaches, driving measurable business impact while collaborating with cross‑functional teams and leadership.

Qualifications

• 6+ 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 containerised 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

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

Skills

Python

R

SQL

ML algorithms

Big data engineering

Kubernetes

Airflow

Spark

Agentic AI

Data visualization

Education

Bachelor's or Master's degree in CS / related field

Tools

Docker

Git

Jenkins

Mlflow

LangGraph

LangChain

Kubernetes

AWS S3

Redshift

Job description

About Citi: Citi, the leading global bank, has approximately 200 million customer accounts and does business in more than 160 countries and jurisdictions. Citi provides consumers, corporations, governments, and institutions with a broad range of financial products and services, including consumer banking and credit, corporate and investment banking, securities brokerage, transaction services, and wealth management. As a bank with a brain and a soul, Citi creates economic value that is systemically responsible and in our clients’ best interests. As a financial institution that touches every region of the world and every sector that shapes your daily life, our Enterprise Operations & Technology teams are charged with a mission that rivals any large tech company. Our technology solutions are the foundations of everything we do from keeping the bank safe, managing global resources, and providing the technical tools our workers need to be successful to designing our digital architecture and ensuring our platforms provide a first‑class customer experience. We reimagine client and partner experiences to deliver excellence through secure, reliable, and efficient services.

Our commitment to diversity includes a workforce that represents the clients we serve from all walks of life, backgrounds, and origins. We foster an environment where the best people want to work. We value and demand respect for others, promote individuals based on merit, and ensure opportunities for personal development are widely available to all. Ideal candidates are innovators with well‑rounded backgrounds who bring their authentic selves to work and complement our culture of delivering results with pride. If you are a problem solver who seeks passion in your work, come join us. We’ll enable growth and progress together.

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: 6+ 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 containerised 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 modelling (LDA), 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.

This job description provides a high‑level review of the types of work performed. Other job‑related duties may be assigned as required.

Job Family Group: Technology

Job Family: Data Science

Time Type: Full time

Primary Location Full Time Salary Range: $120,800.00 - $170,800.00

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.