AI-Enabled Business Analyst

Attention Arc•Irvington, NY

2d•$100,000 - $120,000•Hybrid

About The Position

The AI-Enabled Business Analyst works within Attention Arc’s Technology & Analytics team to improve agency-wide operations by combining business analysis, data expertise, and practical AI knowledge. The role focuses on recurring challenges across departments and client teams, developing reusable and scalable solutions rather than addressing only account-specific media performance. The analyst works directly with leaders, managers, and employees to understand workflows, decision-making, and operational needs, then translates those needs into practical improvements involving data, AI, automation, and process redesign. The position uses SQL and Snowflake to work with data from multiple systems, and applies AI tools and workflow automation to make information more accessible, reduce repetitive work, and improve decision-making. It includes process mapping, data integration and validation, prompt development, prototype creation, solution testing, documentation, training, and ongoing measurement of results. Success is measured through improvements such as time saved, reduced manual effort, better accuracy, faster turnaround, and employee adoption. The role requires the ability to communicate technical concepts clearly to nontechnical stakeholders and to apply responsible AI practices, including human review, privacy, and information security. The position is hybrid, with employees expected to work from the Irvington, New York office three days per week, Tuesday through Thursday. Candidates must reside in and be able to commute to that office. Applicants must be authorized to work in the United States; the company does not sponsor work authorization.

Requirements

2–4 years of professional experience in business analysis, data analysis, business intelligence, process improvement, operations analytics, or a related analytical role.
Strong proficiency in SQL, including joining multiple datasets, filtering and aggregating data, writing complex queries, and validating results.
Hands-on experience with Snowflake or a comparable cloud data warehouse, including querying, combining, and transforming data from multiple sources.
Demonstrated ability to understand business workflows, document processes, identify inefficiencies, and recommend practical improvements.
Experience creating process maps, workflow diagrams, decision trees, or business requirements documentation.
Experience combining data from multiple systems for reporting, analysis, or operational improvements.
Strong Excel or Google Sheets skills, including advanced formulas, data manipulation, and development of analytical tools or calculators.
Practical experience using generative AI tools such as ChatGPT, Claude, Gemini, or Microsoft Copilot for analysis, research, process improvement, or business workflows.
Experience developing and refining AI prompts to produce accurate, structured, and repeatable outputs.
Understanding of data quality, validation, data relationships, and basic data transformation principles.
Ability to translate business needs into analytical requirements, data solutions, and workflow improvements.
Strong analytical reasoning and structured problem-solving skills.
Ability to communicate findings and recommendations clearly to technical and nontechnical stakeholders.
Experience collaborating with stakeholders to gather requirements, test solutions, and implement improvements.
Familiarity with data privacy, information security, and responsible AI practices.
Bachelor’s degree in Business Analytics, Information Systems, Data Analytics, Business Administration, Computer Science, or a related field, or equivalent practical experience.
Must be authorized to work in the United States for any employer.
Must reside in and be able to commute to the Irvington, New York office three days per week.

Nice To Haves

Experience using Python for data manipulation, analysis, scripting, or workflow automation.
Familiarity with data transformation concepts, ETL/ELT processes, and data pipelines.
Experience with business intelligence tools such as Power BI, Tableau, or Looker.
Experience using workflow automation tools such as Power Automate, Zapier, Make, or similar platforms.
Exposure to APIs, system integrations, or automated data workflows.
Experience developing operational dashboards, calculators, planning tools, or standardized reporting processes.
Familiarity with advertising, marketing, media planning, or media agency operations.
Experience working with data from multiple client accounts, departments, or business systems.
Exposure to AI-assisted analysis, document processing, information retrieval, or structured prompt workflows.
Examples of process improvement or data projects demonstrating measurable gains in efficiency, accuracy, or productivity.

Responsibilities

Meet with department leaders, managers, and employees to understand responsibilities, recurring tasks, operational challenges, and objectives.
Analyze workflows to identify manual processes, duplicated effort, inefficiencies, disconnected systems, and improvement opportunities.
Create process maps, workflow diagrams, and decision trees documenting processes and decision-making.
Identify underlying business problems and opportunities to standardize or simplify processes across departments and client teams.
Translate business challenges into requirements for data solutions, AI applications, workflow automations, and process improvements; assess solutions for feasibility and business impact.
Collaborate with stakeholders to test proposed improvements and ensure solutions fit employees’ actual workflows.
Query, join, transform, validate, and organize data from multiple internal and external sources using SQL and Snowflake or comparable tools.
Consolidate information into datasets, reports, and workflows that reduce manual effort and improve accuracy.
Improve recurring data preparation, reconciliation, and reporting processes; analyze data structures and relationships across systems.
Build and improve data-driven tools, calculators, templates, reporting processes, reusable data models, and standardized reporting approaches.
Investigate data quality issues, inconsistent definitions, missing information, and process gaps; document data sources, transformation logic, calculations, and assumptions.
Collaborate with Analytics, Data Engineering, Technology, and other colleagues on integrations and data infrastructure needs.
Identify practical generative AI and large language model use cases, understand process inputs and business context, and develop and refine prompts and repeatable AI-assisted workflows.
Evaluate AI outputs for accuracy, consistency, usefulness, and alignment with business requirements; determine when AI is appropriate versus process redesign or conventional automation.
Explore AI platforms including ChatGPT, Microsoft Copilot, Claude, and Gemini; document successful use cases and apply responsible AI practices, including human review, privacy, and information security.
Identify repetitive work suitable for automation and develop or configure solutions using spreadsheets, SQL, AI platforms, workflow tools, and lightweight scripting where appropriate.
Explore tools such as Power Automate, Zapier, and Make; collaborate with technical colleagues when solutions require complex development, integrations, or engineering support.
Test solutions with end users, monitor workflows, and identify opportunities for continued improvement.
Identify recurring operational challenges across the agency and develop scalable shared tools and processes with departments including Media Planning, Activation, Research, Analytics, Account Services, Ad Operations, Finance, Project Management, and People & Experience.
Support the accessibility, organization, and usefulness of agency data and knowledge; research emerging technologies and develop small-scale prototypes or proofs of concept.
Build relationships with stakeholders, facilitate workflow discussions, translate technical concepts into accessible language, and demonstrate tools and AI-assisted workflows.
Create process documentation, decision trees, user guides, and training materials; gather employee feedback and support responsible adoption of AI and automation.
Define and monitor success measures, establish baselines where possible, collect stakeholder feedback, refine or standardize effective solutions, maintain initiative documentation, and share findings and recommendations with Technology leadership and stakeholders.