Lead AI/ML Data Scientist - VP

Citigroup; Mississauga; On-site; CAD 120,000 - 170,000; Full time

Job summary

Citi is seeking a Lead AI/ML Data Scientist to join the Olympus Data Reconciliation and Engineering team, shaping the next generation of AI capabilities powering enterprise reconciliation across global processing hubs.

In this role you will own the full ML lifecycle from ideation through deployment, delivering measurable impact across Capital Markets operations, risk, and finance, while collaborating with cross-functional teams to ensure scalable, production-ready solutions that meet Citi

Qualifications

• 6+ years of AI/ML development and big data engineering in finance/telecom.
• Proficient in Python, R, and SQL with deep ML algorithm experience.
• Extensive experience deploying Agentic AI and LLM-based solutions.
• Strong ability to translate data insights into strategic recommendations.

Responsibilities

• Design, build, and deploy AI and ML models for enterprise-scale reconciliation.
• Lead end-to-end ML lifecycle from data preprocessing to production integration.
• Analyze large volumes of financial data to uncover trends and opportunities.
• Define ML roadmaps aligned with timelines and Citi's architecture standards.
• Translate complex findings into visualizations for senior leadership.
• Collaborate with engineering, operations, and cross-functional teams for scalable production.
• Identify technology risks and communicate business implications to stakeholders.
• Maintain comprehensive model documentation and knowledge transfer.

Skills

AI/ML development

Big data engineering

Python

SQL

R

LangChain

Agentic AI

LangGraph

TensorFlow

PyTorch

scikit-learn

Tools

TensorFlow

PyTorch

scikit-learn

Pandas

NumPy

LangGraph

LangChain

ADK

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

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