Citi – Data Science Analyst

Job Description:

The Data Science Analyst role at Citi is an excellent entry-level opportunity for aspiring data professionals to build a strong foundation in analytics, machine learning, and data-driven decision-making. As part of Citi’s global analytics team, you will work on real-world financial datasets, helping the organization derive actionable insights that influence business strategies. This role is ideal for candidates who are passionate about data science, eager to learn, and ready to contribute to impactful projects in a structured and collaborative environment.

In this position, you will assist in collecting, cleaning, and analyzing large datasets to ensure data quality and reliability. You will perform exploratory data analysis (EDA), support feature engineering, and contribute to the development of machine learning models under the guidance of experienced data scientists. Additionally, you will work on modern data science applications such as semantic search techniques, Retrieval-Augmented Generation (RAG), and agent-based AI solutions, gaining exposure to cutting-edge technologies in the field.

Citi fosters a culture of continuous learning, mentorship, and innovation, providing a supportive environment for early-career professionals. You will collaborate with cross-functional teams, participate in training programs, and gain hands-on experience with tools and technologies used in large-scale financial analytics. This role offers a clear pathway to becoming a full-fledged Data Scientist, making it a valuable stepping stone for building a long-term career in data science and advanced analytics.

Roles & Responsibilities:

• Assist in collecting, cleaning, and preprocessing large datasets to ensure high data quality and integrity for analysis and modeling purposes. Focus on maintaining accuracy and consistency in data pipelines.
• Perform exploratory data analysis (EDA) to identify trends, patterns, and anomalies, helping teams derive meaningful insights from complex datasets.
• Support feature engineering processes by identifying relevant variables and transforming raw data into structured formats suitable for machine learning models.
• Contribute to the development, training, and evaluation of basic machine learning models under supervision, ensuring proper validation and performance tracking.
• Prepare clear and well-structured documentation of data processes, models, and analytical findings for future reference and knowledge sharing.
• Collaborate with senior data scientists, engineers, and business stakeholders to understand requirements and deliver data-driven solutions aligned with business goals.
• Assist in creating reports, dashboards, and visualizations using tools like Power BI or Tableau to effectively communicate insights to stakeholders.
• Participate in learning programs, workshops, and self-driven initiatives to enhance technical and analytical skills continuously.
• Contribute to innovative projects involving semantic search, RAG frameworks, and agentic AI solutions using tools like LangChain and modern AI platforms.
• Ensure attention to detail in all analytical tasks, maintaining high standards of accuracy, documentation, and data governance.

Requirements & Eligibility:

• Bachelor’s or Master’s degree in a quantitative field such as Computer Science, Statistics, Mathematics, Engineering, or Economics, with a strong academic foundation in analytical concepts.
• Basic proficiency in programming languages like Python or R, including libraries such as Pandas, NumPy, and Scikit-learn for data analysis and modeling.
• Understanding of SQL and database concepts, enabling efficient data extraction, querying, and manipulation from structured datasets.
• Knowledge of fundamental statistical concepts such as hypothesis testing, regression analysis, and probability distributions.
• Familiarity with data visualization tools or libraries like Matplotlib, Seaborn, Tableau, or Power BI to present insights effectively.
• Conceptual understanding of machine learning algorithms such as Linear Regression, Logistic Regression, and Decision Trees.
• Strong analytical and problem-solving skills, with the ability to interpret data and derive meaningful conclusions.
• Excellent communication skills, both written and verbal, to explain technical concepts clearly to non-technical stakeholders.
• High learning agility and curiosity to explore new tools, technologies, and methodologies in the rapidly evolving data science field.
• Exposure to version control systems (Git), cloud platforms (AWS, Azure, GCP), or big data technologies (Spark, Hadoop) is preferred and adds value.

Expected Salary:

For a Data Science Analyst (entry-level/trainee role) at top financial institutions like Citi in Bangalore, the salary typically ranges between ₹6 LPA to ₹14 LPA depending on educational background, technical skills, and internship/project experience. Additional benefits such as bonuses, training programs, and career growth opportunities further enhance the overall compensation package.

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