Lead AI/ML Data Scientist - VP

Citi • Mississauga, ON

8d • CA$120,800 - CA$170,800

About The Position

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.

Requirements

• 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 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
• Strong background in data reconciliation frameworks, data quality validation, and ETL/ELT pipelines for financial data processing at enterprise scale
• Bachelor’s or Master’s degree in Computer Science, Data Science, Software Engineering, Information Systems, Mathematics, Statistics or related fields of study.

Nice To Haves

• AWS SageMaker, Azure ML, or Google Vertex AI (beneficial)
• 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

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.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior