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Lead AI/ML Data Scientist- Vice president at Citigroup · Chennai…
Job Description
Lead AI/ML Data Scientist- Vice president
Location: Chennai Tamil Nadu India · Hybrid
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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Job Family Group:
Technology------------------------------------------------------
Job Family:
Data Science------------------------------------------------------
Time Type:
Full time------------------------------------------------------
Most Relevant Skills
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Other Relevant Skills
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