Business Analytics Analyst - Pyspark

Technical / Tools Proficiency

• SQL: Advanced querying (joins, window functions, CTEs, performance tuning) across large relational/Teradata/Hive data warehouses.
• Python: Data wrangling (pandas/numpy), automation scripting, statistical analysis, and visualization (matplotlib/seaborn).
• PySpark: Distributed data processing for large campaign/transaction datasets, working within Hadoop/Spark ecosystems.
• SAS: Base SAS and SAS Macros for large-scale data manipulation, campaign tagging, and reporting automation.

Domain Knowledge - Banking & Card Products

• Solid grounding in consumer banking/credit card business fundamentals: account lifecycle, interest income, fees, credit risk basics, and portfolio economics.
• Understanding of the credit card marketing lifecycle: acquisition, activation, usage, retention, and attrition.

Campaign Analytics & Measurement

• Design and execution of test-vs-control and champion-challenger frameworks for campaign effectiveness.
• Incrementality/lift analysis, response modeling, and uplift measurement.
• Campaign performance tracking: response rates, conversion, activation, spend, and profitability metrics.
• Experience building recurring performance dashboards/scorecards for campaign monitoring and post-campaign readouts.

Statistical & Quantitative Analysis

• Hypothesis testing, A/B testing methodology, and significance testing.
• Segmentation and targeting analysis (behavioral, demographic, risk-based).
• Regression/predictive modeling exposure (logistic regression, decision trees) for propensity/response scoring is a plus.
• Data quality checks, reconciliation, and root-cause/variance analysis.

GenAI / Emerging Tools Familiarity

• Working knowledge of Generative AI tools for analytics productivity - e.g., using LLM-based assistants for code generation/debugging (SAS/SQL/Python), documentation, and summarization of campaign results.
• Exposure to prompt engineering basics for querying enterprise GenAI platforms (e.g., internal chat/assist tools) to accelerate exploratory analysis and reporting.
• Awareness of GenAI use cases in marketing analytics: automated insight generation, natural-language querying of datasets and synthetic test data creation.
• Curiosity/adaptability to continuously evaluate and adopt new AI-assisted analytics tools as the function modernizes its toolkit.

Communication

• Ability to translate analytical findings into actionable business recommendations for marketing/product stakeholders.
• Strong storytelling with data - clear presentation of insights to senior stakeholders (PowerPoint/Excel proficiency).
• Cross-functional collaboration with Marketing, Risk, Finance, and Technology teams.
• Project management skills to manage multiple concurrent campaign analytics requests under tight timelines.

Behavioral Attributes

• Independent ownership of analytics workstreams with minimal supervision, commensurate with 5-7 years of hands-on experience.
• Mentoring junior analysts and reviewing their analytical output.
• Strong attention to detail and controls mindset given regulatory sensitivity of rate-based offers.
• Proactive problem-solving and ability to work in ambiguous, fast-paced environments.

Experience Level: 5-7 years of relevant analytics experience, preferably in banking/cards/financial services.

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