{"id":3017095,"url":"https://alion.io/job/citi-business-analytics-analyst-pyspark","title":"Business Analytics Analyst - Pyspark","company":{"id":7841,"name":"Citi","domain":"citi.com","url":"https://alion.io/company/citi","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":100,"open_postings":528,"ghost_share":0.006,"stale_share":0.155,"repost_share":0.07,"time_to_fill_p50_days":9,"computed_at":"2026-11-03T05:54:40Z"}},"role":"Analytics","role_family":"Analytics","seniority":"senior","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, 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Engineering","optional":true}],"status":"live","first_seen_at":"2026-10-16T00:00:00Z","employer_posted_date":"2026-10-16","last_verified_at":"2026-11-03T13:09:23Z","board_verified":true,"closed_at":null,"days_open":18,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":18},"description":"Technical / Tools Proficiency\n SQL: Advanced querying (joins, window functions, CTEs, performance tuning) across large relational/Teradata/Hive data warehouses.\n Python: Data wrangling (pandas/numpy), automation scripting, statistical analysis, and visualization (matplotlib/seaborn).\nPySpark: Distributed data processing for large campaign/transaction datasets, working within Hadoop/Spark ecosystems.\nSAS: Base SAS and SAS Macros for large-scale data manipulation, campaign tagging, and reporting automation.\nDomain Knowledge - Banking & Card Products\nSolid grounding in consumer banking/credit card business fundamentals: account lifecycle, interest income, fees, credit risk basics, and portfolio economics.\nUnderstanding of the credit card marketing lifecycle: acquisition, activation, usage, retention, and attrition.\nCampaign Analytics & Measurement\nDesign and execution of test-vs-control and champion-challenger frameworks for campaign effectiveness.\nIncrementality/lift analysis, response modeling, and uplift measurement.\nCampaign performance tracking: response rates, conversion, activation, spend, and profitability metrics.\nExperience building recurring performance dashboards/scorecards for campaign monitoring and post-campaign readouts.\nStatistical & Quantitative Analysis\nHypothesis testing, A/B testing methodology, and significance testing.\nSegmentation and targeting analysis (behavioral, demographic, risk-based).\nRegression/predictive modeling exposure (logistic regression, decision trees) for propensity/response scoring is a plus.\nData quality checks, reconciliation, and root-cause/variance analysis.\nGenAI / Emerging Tools Familiarity\nWorking 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.\nExposure to prompt engineering basics for querying enterprise GenAI platforms (e.g., internal chat/assist tools) to accelerate exploratory analysis and reporting.\nAwareness of GenAI use cases in marketing analytics: automated insight generation, natural-language querying of datasets and synthetic test data creation.\nCuriosity/adaptability to continuously evaluate and adopt new AI-assisted analytics tools as the function modernizes its toolkit.\nCommunication\nAbility to translate analytical findings into actionable business recommendations for marketing/product stakeholders.\nStrong storytelling with data - clear presentation of insights to senior stakeholders (PowerPoint/Excel proficiency).\nCross-functional collaboration with Marketing, Risk, Finance, and Technology teams.\nProject management skills to manage multiple concurrent campaign analytics requests under tight timelines.\nBehavioral Attributes\nIndependent ownership of analytics workstreams with minimal supervision, commensurate with 5-7 years of hands-on experience.\nMentoring junior analysts and reviewing their analytical output.\nStrong attention to detail and controls mindset given regulatory sensitivity of rate-based offers.\nProactive problem-solving and ability to work in ambiguous, fast-paced environments.\nExperience Level: 5-7 years of relevant analytics experience, preferably in banking/cards/financial services.\n------------------------------------------------------\nJob Family Group:\nDecision Management------------------------------------------------------\nJob Family:\nBusiness Analysis------------------------------------------------------\nTime Type:\nFull time------------------------------------------------------\nMost Relevant Skills\nPlease see the requirements listed above.------------------------------------------------------\nOther Relevant Skills\nFor complementary skills, please see above and/or contact the recruiter.------------------------------------------------------\nCiti is an equal opportunity employer, and qualified candidates will receive consideration without regard to their race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other characteristic protected by law.\nIf you are a person with a disability and need a reasonable accommodation to use our search tools and/or apply for a career opportunity review Accessibility at Citi.\nView Citi’s EEO Policy Statement and the Know Your Rights 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