Citi Hiring: Data Science Analyst 2026:-
Citi is hiring experienced professionals for the position of Data Science Analyst in Haryana, India. Candidates with strong experience in Python, PySpark, SQL, Machine Learning, Deep Learning and Generative AI can explore this opportunity.
| Company: | Citi |
| Job Role: | Data Science Analyst |
| Job Type: | Full-Time |
| Experience: | 6–7 Years (Bachelor’s Degree) / 4–5 Years (Master’s Degree) / PhD |
| Salary: | As per company Standards |
| Posted Date: | 5 November 2026 |
| Application Deadline: | ASAP |
Role Overview:-
The Data Science Analyst will work as part of the Data Science and Modeling team within Citi’s North America Consumer Bank.
The role focuses on developing advanced analytical solutions, predictive models, Machine Learning applications and Generative AI solutions to solve complex business problems.
You will work with large datasets using Python, PySpark, SQL, Hive and other technologies to build, fine-tune, validate and deploy AI-driven models.
The position also involves developing Large Language Model (LLM) applications, Retrieval-Augmented Generation (RAG) solutions, model governance and collaboration with business stakeholders.
Educational Requirements:-
Candidates must meet one of the following qualification and experience combinations:
- Bachelor’s Degree: 6–7 years of experience in Data Analytics.
- Master’s Degree: 4–5 years of experience in Data Analytics.
- PhD: Eligible as per the job description.
Preferred Educational Background:
- B.E. / B.Tech – Engineering
- M.E. / M.Tech – Engineering
- B.Sc. / M.Sc. – Statistics or Mathematics
- Bachelor’s / Master’s in Economics
- Information Technology
- Computer Applications
- Other relevant quantitative disciplines from a premier institute.
Key Skills:-
1. Technical Skills
- Python Programming
- PySpark
- R Programming
- Advanced SQL
- Apache Hive
- Machine Learning
- Deep Learning
- Generative AI (GenAI)
- Large Language Models (LLMs)
- Retrieval-Augmented Generation (RAG)
- Prompt Engineering
- Model Fine-Tuning
- XGBoost
- Linear Regression
- Logistic Regression
- Predictive Modelling
- Statistical Analysis
- Data Visualization
- Model Validation and Deployment
- Model Governance
- Big Data Technologies
2. Data Science & Analytical Skills
- Exploratory Data Analysis (EDA)
- Hypothesis Testing
- Customer Segmentation
- Predictive Analytics
- Statistical Modelling
- Data Collection and Cleaning
- Data Processing
- Large Dataset Management
- Feature Engineering
- Model Performance Evaluation
- Credit Risk Analytics
- Customer Retention Analytics
- Marketing Analytics
3. Banking & Functional Skills
- Retail Banking
- Credit Card Analytics
- Customer Acquisition
- Customer Relationship Management
- Customer Retention
- Product Development
- Pricing Optimization
- Payment Optimization
- Digital Banking
- Anti-Money Laundering (AML)
- Fair Lending Compliance
- Model Risk Management
4. Soft Skills
- Strong analytical and problem-solving abilities
- Excellent verbal and written communication
- Stakeholder management
- Project management
- Team collaboration
- Leadership and mentoring abilities
- Critical thinking
- Self-motivation
- Adaptability and willingness to learn
- Risk awareness
- Ability to communicate technical concepts to senior management
Roles & Responsibilities:-
1. Data Management & Analysis
- Work with large and complex datasets using Python, PySpark, SQL, Hive and other technologies.
- Document data requirements for analytical and modelling projects.
- Perform data collection, processing, cleaning and exploratory data analysis.
- Apply statistical analysis and data visualization techniques to generate business insights.
- Identify patterns and opportunities from large-scale customer and transaction datasets.
2. Machine Learning & Deep Learning
- Develop and implement Machine Learning and Deep Learning solutions for various business requirements.
- Build predictive models using advanced modelling techniques.
- Apply algorithms such as XGBoost, Linear Regression, Logistic Regression and customer segmentation.
- Perform model training, validation and performance evaluation.
- Maintain and validate models deployed in production environments.
3. Generative AI & LLM Development
- Design, fine-tune and implement Large Language Model (LLM) applications.
- Develop Generative AI solutions using prompt engineering techniques.
- Implement Retrieval-Augmented Generation (RAG) applications.
- Perform model fine-tuning to improve performance for specific business requirements.
- Collaborate with teams to build AI-powered business solutions.
- Explore emerging Generative AI technologies and applications.
4. Model Deployment & Governance
- Support model deployment and ongoing maintenance.
- Perform model validation and monitoring.
- Work with model governance and fair lending teams.
- Ensure models comply with Citi’s internal standards and applicable regulations.
- Maintain appropriate model documentation and reporting.
- Identify and escalate model-related risks and control issues.
5. Business Analytics & Banking Solutions
- Develop analytical solutions for customer acquisition and customer retention.
- Support existing customer management and product development initiatives.
- Apply predictive modelling to pricing and payment optimization.
- Develop analytical solutions for digital customer journeys.
- Apply Machine Learning and Generative AI techniques to marketing, risk, digital banking and AML domains.
6. Project Management & Team Collaboration
- Own and deliver multiple complex analytical projects.
- Convert business requirements into analytical and modelling frameworks.
- Coordinate with business partners and cross-functional teams.
- Communicate analytical findings to peers and senior management.
- Coach and mentor junior team members.
- Contribute to training, competency development and organizational initiatives.
7. Risk Management & Compliance
- Assess risks associated with business and analytical decisions.
- Follow applicable laws, regulations and internal policies.
- Maintain professional and ethical standards.
- Protect the firm’s reputation, clients and business assets.
- Escalate and report control issues transparently.
About Company:-
Citi is a global financial services organization that provides banking, credit cards, financial markets and wealth management services to individuals, businesses and institutions.
Citi aims to serve as a trusted financial partner by providing responsible financial services that support economic growth and progress.
With approximately 219,000 employees worldwide, Citi offers opportunities to work with global teams, advanced technologies and innovative financial solutions.
About Analytics and Information Management (AIM):-
Citi’s Analytics and Information Management (AIM) division was established in 2003 and operates across multiple Indian cities, including Bengaluru, Chennai, Pune and Mumbai.
The AIM team analyzes business and customer information to generate actionable insights, identify revenue opportunities and support business decisions through advanced analytical methodologies.
The North America Consumer Bank – Data Science and Modeling team works with millions of prospects and billions of customer transactions using big data technologies, Machine Learning, Artificial Intelligence and Deep Learning.
Citi Hiring: Data Science Analyst 2026 Application Process:-
Apply Link:- Click Here To Apply
Note:- Only the shortlisted candidates will be notified for the further interview process.
