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.

Job Family Group: Decision Management

Job Family: Business Analysis

Time Type: Full time

Most Relevant Skills Please see the requirements listed above.

Other Relevant Skills For complementary skills, please see above and/or contact the recruiter.

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