Machine Learning Analyst- Python and SQL

Candidates typically undergo a three‑round interview process: an initial technical screen focusing on Python, SQL, and basic ML concepts; a second round that dives deeper into data modeling, case studies, and coding challenges; and a final HR round that assesses cultural fit and career goals. The interviews are known to be rigorous but fair, with a strong emphasis on problem‑solving and communication skills.

Round 1: Technical interview covering Python, SQL, and ML concepts → Round 2: Deep technical interview with case study and coding challenge → Round 3: HR interview focusing on cultural fit and career aspirations

Citi promotes a collaborative and inclusive work environment where employees are encouraged to innovate and take ownership of projects. The company values work‑life balance and offers flexible working arrangements. Career growth is supported through mentorship, continuous learning, and exposure to global projects.

Bachelor’s degree in Computer Science, IT, or related field with at least 3 years of professional experience, or Master’s degree with 2 years of experience. Minimum 60% aggregate in the qualifying degree. Candidates must meet Citi’s backlog policy and be eligible for employment in India.

Citi, one of the world’s largest financial institutions, has a rich legacy of 200 years in providing innovative banking solutions. With a global footprint spanning over 200 countries, Citi empowers millions of customers and businesses to achieve their financial goals. In India, Citi’s presence is strong across major metros, offering a blend of traditional banking services and cutting‑edge digital products. The company is known for its inclusive culture, emphasis on continuous learning, and a commitment to responsible growth. The Machine Learning Analyst role is situated within the Analytics and Information Management (AIM) team, specifically the North America Consumer Bank – Customer Experience (CX) Analytics function. This team focuses on understanding and enhancing customer interactions across all touchpoints. As a Machine Learning Analyst, you will work with large, complex data sets to uncover insights that drive customer satisfaction and business performance. Your work will directly influence product decisions, process improvements, and strategic initiatives. Key responsibilities include: 1. Collecting, cleaning, and preprocessing internal and external data using SQL and Python. 2. Building predictive models and statistical analyses to forecast customer behavior and identify pain points. 3. Developing and deploying machine learning pipelines, including PySpark and NLP models, to support real‑time decision making. 4. Creating data visualizations and dashboards to communicate findings to stakeholders. 5. Collaborating with cross‑functional teams (product, engineering, CX) to translate insights into actionable solutions. 6. Managing model lifecycle – from version control with Git to automated deployment via Jenkins. 7. Ensuring compliance with data governance, security, and regulatory requirements. 8. Participating in sprint cycles, providing technical guidance, and mentoring junior analysts. 9. Staying updated on emerging technologies such as Large Language Models and prompt engineering. 10. Documenting data requirements, processes, and best practices for future reference. Tech stack: Python, SQL, PySpark, NLP, LLM/Gen AI, Git, Jenkins, API integration, data visualization tools, statistical modeling libraries. Growth path: Starting as a data analyst, you can progress to senior analyst, lead data scientist, or product analytics manager. Citi’s global network offers opportunities to work on cross‑regional projects, pursue certifications, and attend leadership development programs. Why join Citi? The company offers competitive compensation, a collaborative environment, and a clear career trajectory. Employees benefit from extensive training resources, mentorship, and the chance to work on high‑impact projects that shape the future of banking.