Campaign Analytics & AI Enablement Specialist

Citi is looking for a Data & Information Management Analyst to join a team that turns complex data challenges into clear, actionable intelligence that directly shapes business decisions. In this role, you will extract, analyze, and automate data workflows across large-scale databases, delivering reliable outputs that support critical initiatives across multiple business units. Your work will have a direct impact on the quality and timeliness of insights that drive performance across the team and its closest partners.

## What the Role Is

We are hiring a Campaign Analytics & AI Enablement Specialist (C11) to join our Wealth Campaign Analytics, Execution & Optimization team.

In this hybrid role, you will be the analytical owner and operational lead for customer campaign programs across wealth products (e.g., Investment Products, Wealth Advisory, Mortgages, FX, Time Deposits, and Digital Banking). You will combine core data execution, performance analytics, and campaign optimization with applied AI enablement—leveraging modern GenAI tools, prompt and context engineering, knowledge base setup, retrieval optimization, Text-to-SQL assistants, and NLP to design, automate, execute, and continually optimize high-impact marketing and client engagement campaigns.

As a C11-level specialist, you will bridge the gap between business stakeholders and data platforms, maintaining rigorous data governance while championing AI-assisted productivity and analytical optimization within the team.

Who You Are

• AI-Augmented Data Practitioner: You actively leverage modern GenAI tools, conversational assistants, context engineering, and prompt design to supercharge analytics, code generation, and campaign operations.
• Campaign Orchestrator & Optimizer: You excel at translating complex business requirements into high-performance data extraction queries using SQL, PySpark, and Python, continuously refining targeting logic to maximize campaign conversion and ROI.
• Analytical Problem Solver: You apply structured analytical techniques—such as funnel diagnostics, test-versus-control lift analysis, and cohort evaluation—to uncover performance drivers and optimize marketing channels.
• Workflow Innovator: You proactively automate repetitive processes—using AI for Text-to-SQL generation, automated insight summarization, knowledge base curation, and content variant ideation.
• Disciplined & Version-Controlled: You manage analytical assets, queries, and automation scripts using Git / GitHub for robust code collaboration and version control.
• Process & Controls Oriented: You uphold strict campaign gating criteria, contact fatigue management, customer privacy regulations, and risk/compliance controls.

What You Do (Core Responsibilities)

1\. Campaign Execution & Data Pipeline Management

• Partner with Wealth Product Managers, Marketing, and Relationship Manager (RM) teams to translate campaign objectives into robust technical targeting criteria.
• Develop, test, and execute data extraction queries and pipelines using SQL, PySpark, and Python to generate qualified lead lists.
• Enforce campaign gating, contact frequency rules, suppression logic (e.g., Do-Not-Contact lists, regulatory opt-outs), and eligibility filters across digital and RM channels.
• Manage end-to-end campaign delivery calendars, ensuring 100% on-time execution of scheduled and trigger-based wealth programs.

2\. Campaign Analytics, Experimentation & Optimization

• Performance & Funnel Analytics: Analyze campaign engagement funnels (Targeted ➔ Delivered ➔ Engaged ➔ Converted ➔ Balance/NNA Impact) to diagnose drop-offs and identify growth opportunities.
• Experimentation & Lift Measurement: Design and execute test-versus-control experimental frameworks and A/B test splits to isolate incremental campaign lift and revenue impact.
• Targeting & Channel Optimization: Analyze response patterns across customer segments and delivery channels (Mobile App In-App/Push, Online Banking, Email, SMS, RM CRM) to recommend targeting refinements and channel mix optimization.
• Interactive Visualizations: Build and maintain interactive performance tracking dashboards and diagnostic tools in Tableau / Power BI with automated underlying data feeds.

3\. GenAI Enablement, Context Engineering & Workflow Automation

• Context Engineering & Knowledge Base Setup: Organize, structure, and curate internal campaign rules, data dictionaries, and targeting playbooks into searchable knowledge bases to enable high-accuracy AI retrieval and team knowledge sharing.
• Retrieval Optimization: Optimize data retrieval contexts and prompt structures to ensure conversational AI and retrieval-augmented tools deliver precise, hallucination-free analytical outputs and code.
• Text-to-SQL & Natural Language Processing (NLP): Apply Text-to-SQL interfaces and NLP techniques to accelerate ad-hoc data exploration, query generation, and unstructured client feedback/interaction analysis.
• AI-Assisted Code Acceleration: Utilize GenAI coding tools to rapidly draft, optimize, and debug complex SQL queries, PySpark data transformations, and Python scripts.
• Automated Campaign Insights Summarization: Leverage GenAI pipelines to generate executive narrative summaries, performance commentary, and anomaly explanations from campaign outcome data.
• Content & Message Variant Ideation: Assist marketing partners by using Generative AI to generate personalized messaging copy variations, email subject lines, and RM talking point templates tailored to specific client segments.

4\. Code Collaboration, QA & Data Governance

• Manage and version-control all campaign scripts, SQL queries, and configuration files using Git / GitHub.
• Perform thorough Quality Assurance (QA) validations—such as row count reconciliations, schema validations, null checks, and deduplication—before releasing leads to execution channels.
• Maintain comprehensive documentation of campaign metadata, targeting criteria, and data lineage.

Key Deliverables

• Target Audience Datasets / Leads: High-precision, fully validated target lead files delivered to digital execution engines and CRM platforms.
• Campaign Delivery Calendar: Well-managed schedule of recurring, trigger-based, and ad-hoc wealth campaign programs.
• Optimization & Lift Analysis Decks: Analytical performance decks isolating incremental conversion, Net New Assets (NNA), and channel efficiency.
• Interactive Performance Dashboards: Automated Tableau / Power BI dashboards for campaign funnel monitoring and stakeholder self-service.
• AI-Assisted Summaries & Knowledge Bases: Curated campaign knowledge bases, prompt templates, and automated executive performance briefs.
• Versioned Code Repository: Well-documented, reusable SQL, PySpark, and Python scripts maintained in GitHub.

Qualifications & Skills:

Education & Experience

• Education: Bachelor’s degree in Computer Science, Information Technology, Engineering, Statistics, Mathematics, Economics, or related quantitative field.
• Experience: 5–8 years of hands-on experience in Campaign Analytics, Marketing Analytics, Business Intelligence, or Customer Data Operations within Banking / Financial Services (Wealth Management or Retail Banking preferred).

Technical, Analytics & Campaign Optimization Skills

• Database & Querying: Strong hands-on proficiency in SQL (complex joins, CTEs, window functions, and query execution plan optimization).
• Big Data & Scripting: Practical working knowledge of Python and PySpark for data manipulation, pipeline automation, and analytical dataset creation.
• Campaign Analytics & Optimization: Strong expertise in campaign performance diagnostics, test vs. control lift measurement, funnel conversion analysis, and targeting rule optimization.
• BI & Visual Analytics: Proven experience designing executive-level interactive performance dashboards in Tableau or Power BI.
• Version Control: Solid understanding and practical usage of Git / GitHub for code repository management, branching, and versioning.
• Platforms: Familiarity with enterprise campaign management tools (e.g., Covenant, Unica, Salesforce Marketing Cloud, Adobe Experience Platform) and Dataiku DSS is an advantage.

AI Knowledge & GenAI Productivity Skills

• AI Literacy & Foundations: Solid conceptual understanding of modern Artificial Intelligence, Large Language Models (LLMs), and Conversational AI capabilities.
• Context Engineering & Knowledge Base Setup: Experience setting up, organizing, and structuring domain knowledge bases, data schemas, and contextual documentation for AI-assisted workflows.
• Retrieval Optimization: Understanding of retrieval optimization techniques to feed relevant context into LLMs for accurate code generation and data querying.
• Prompt Engineering: Demonstrated ability to craft structured, multi-turn, and few-shot prompts for data analysis, code generation, and content drafting.
• Text-to-SQL & NLP: Practical familiarity with Text-to-SQL tools and Natural Language Processing (NLP) techniques for data extraction, schema querying, and text analysis.
• GenAI-Assisted Development: Experience utilizing AI coding tools to accelerate SQL/Python script writing, query refactoring, and automated debugging.
• AI-Driven Insights & Copy Generation: Practical experience using GenAI to summarize campaign outcome metrics into narrative insights and brainstorm personalized copy variations.

Soft Skills

• Excellent communication and stakeholder management skills with ability to translate business goals into analytical solutions.
• Strong problem-solving orientation with attention to detail, data accuracy, and regulatory compliance.

Proactive mindset with a keen interest in adopting and propagating modern AI productivity tools within the team

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## Job Family Group:

Decision Management

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## Job Family:

Data/Information Management

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## Time Type:

Full time

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## Most Relevant Skills

Please see the requirements listed above.

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## Other Relevant Skills

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

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