Business Analytics Analyst - Pyspark

Overview

Business Analytics Analyst focused on PySpark-based campaign and banking analytics, owning analytics workstreams with minimal supervision.

What you'll do

• Design and execute test-vs-control and champion-challenger frameworks for campaign effectiveness.
• Perform incrementality/lift analysis, response modeling, and uplift measurement.
• Track campaign performance metrics including response rates, conversion, activation, spend, and profitability.
• Build recurring performance dashboards and scorecards for campaign monitoring and post-campaign readouts.
• Perform data quality checks, reconciliation, and root-cause/variance analysis.
• Translate analytical findings into actionable business recommendations for marketing and product stakeholders.
• Mentor junior analysts and review their analytical output.

What you'll need

• 5-7 years of relevant analytics experience, preferably in banking, cards or financial services.
• Advanced SQL querying (joins, window functions, CTEs, performance tuning) across large relational/Teradata/Hive data warehouses.
• Python for data wrangling (pandas/numpy), automation scripting, statistical analysis, and visualization (matplotlib/seaborn).
• PySpark for distributed data processing of large campaign/transaction datasets within Hadoop/Spark ecosystems.
• Base SAS and SAS Macros for large-scale data manipulation, campaign tagging, and reporting automation.
• Solid grounding in consumer banking/credit card business fundamentals, including account lifecycle, interest income, fees, credit risk basics, and portfolio economics.
• Understanding of the credit card marketing lifecycle: acquisition, activation, usage, retention, and attrition.
• Hypothesis testing, A/B testing methodology, and significance testing.
• Segmentation and targeting analysis (behavioral, demographic, risk-based).
• Working knowledge of Generative AI tools for analytics productivity, such as LLM-based assistants for code generation/debugging, documentation, and summarization of campaign results.
• Ability to translate analytical findings into actionable business recommendations for marketing/product stakeholders.
• Strong storytelling with data and clear presentation of insights to senior stakeholders (PowerPoint/Excel proficiency).
• Project management skills to manage multiple concurrent campaign analytics requests under tight timelines.
• Strong attention to detail and a controls mindset given regulatory sensitivity of rate-based offers.
• Proactive problem-solving and ability to work in ambiguous, fast-paced environments.

Nice to have

• Regression/predictive modeling exposure (logistic regression, decision trees) for propensity/response scoring.
• Exposure to prompt engineering basics for querying enterprise GenAI platforms to accelerate exploratory analysis and reporting.
• Awareness of GenAI use cases in marketing analytics, such as automated insight generation, natural-language querying of datasets and synthetic test data creation.
• Curiosity and adaptability to continuously evaluate and adopt new AI-assisted analytics tools.

Details

• Location: Bangaluru, Karnataka, India.
• Employment type: Full time.
• Cross-functional collaboration with Marketing, Risk, Finance, and Technology teams.