Lead AI/ML Data Scientist- Vice president

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

Job summary

Citibank (Switzerland) AG is seeking a Lead AI/ML Data Scientist (Vice President) in Chennai to drive the full lifecycle of model development, from ideation to deployment, across global processing hubs. You will lead design, productionization, and adoption of AI/ML capabilities in capital markets operations, risk, and finance.

The role demands 10+ years in AI/ML and big data within finance or related sectors, deep expertise in Python, R, SQL, and distributed computing, and hands-on experience

Qualifications

• 10+ years of AI/ML development and big data engineering in financial services or related sectors.
• Expert Python (scikit-learn, TensorFlow, PyTorch, Pandas, NumPy) and SQL skills incl. PostgreSQL/Oracle/MySQL.
• Strong experience with supervised/unsupervised ML algorithms and production ML lifecycle.

Responsibilities

• Design, build, and deploy AI/ML models at enterprise scale including Generative AI and agent-based solutions.
• Lead end-to-end ML lifecycle from requirements to production deployment and adoption.
• Analyze large financial datasets to extract trends and optimization opportunities across platforms.
• Define ML roadmaps aligned with timelines, budgets, and architecture standards.

Skills

Python

R

SQL

ML algorithms

Agentic AI

LLM-based solutions

LangGraph

LangChain

ADK

Airflow

Kubernetes

Docker

Spark

Hadoop

Hive

Redshift

SageMaker

Git/GitHub

MLflow

Education

Bachelor’s or Master’s degree in CS/DS/ SWE/IS/Math/Stats

Tools

PostgreSQL

Oracle

MySQL

TensorFlow

PyTorch

scikit-learn

Pandas

NumPy

MLflow

Weights & Biases

DVC

Job description

## Lead AI/ML Data Scientist- Vice presidentApplyremote type: Hybridlocations: Chennai Tamil Nadu Indiatime type: Full timeposted on: Posted Todayjob requisition id: 26982421**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.*