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CoSourcing Partners Inc. seeks a Business Analyst – Agentic AI & Automation, focusing on chat customer experiences in Stamford, CT.
This hands-on role requires turning business problems into detailed PRDs, customer journeys, and development-ready user stories for intelligent automation initiatives. You will analyze customer interactions with SQL, validate opportunities with operational data, and craft measurable outcomes.
Position: Business Analyst – Agentic AI & Automation, Chat Customer Experience
Functional Role: Hands-On Product Owner / Business Analyst – Digital Servicing and Customer Experience
Location: Stamford, Connecticut
Work Arrangement: 100% Onsite
Start Date: Immediate Availability Required
Background Check: Mandatory
Core Capabilities: Product Requirements Documents (PRDs), SQL, Customer Journey Analysis, Agentic AI, AI‑Assisted Automation, Python, APIs, Agile Delivery, Product Analytics
The Business Analyst – Agentic AI & Automation will support the discovery, definition, development, and delivery of intelligent automation and agentic AI capabilities for Digital Servicing and Customer Experience, with an emphasis on customer chat and service interactions.
This is a hands‑on, analytically driven product role requiring the ability to move independently from identifying a business problem to defining product requirements, validating opportunities with operational data, and developing practical AI‑assisted solutions.
The individual will create comprehensive Product Requirements Documents (PRDs), translate business needs into customer journeys and development‑ready user stories, analyze customer interaction data, and define measurable business outcomes. The role also requires hands‑on automation capabilities using approved AI‑assisted coding tools, SQL, Python, APIs, or similar technologies.
Working with engineering, architecture, product, and business stakeholders, the individual will help define agentic workflows, including integration requirements, tool execution, fallback handling, human escalation, testing, and operational evaluation.
Success will be measured by the quality and completeness of product requirements, accuracy of business analysis, effectiveness of delivered automations, and demonstrated improvements in customer experience or operational efficiency.
Help transform digital customer servicing by identifying friction in customer interactions and translating those problems into intelligent, measurable solutions. Your work will connect customer needs, operational data, product design, and emerging agentic AI capabilities to improve how customers receive assistance and how servicing teams operate.
1. Independently Define Development‑Ready Agentic AI Product Requirements. take ownership of assigned Digital Servicing and Customer Experience use cases and translate business problems into comprehensive Product Requirements Documents, customer journeys, functional requirements, user stories, and acceptance criteria. Define the expected business value, success measures, dependencies, integration needs, tool‑execution behavior, fallback conditions, and human‑handoff requirements for each applicable agentic AI use case. Success will be measured by stakeholder acceptance of PRDs, readiness of requirements for development, traceability of acceptance criteria, and reduction of avoidable clarification or rework.
2. Identify and Quantify Customer Experience and Automation Opportunities. independently query and analyze customer interaction and operational data using SQL and approved analytical tools to identify servicing friction, recurring issues, automation opportunities, and performance gaps. Establish baseline measures for assigned use cases and translate analytical findings into evidence‑based product recommendations. Success will be measured by accuracy and reproducibility of analyses, acceptance of recommendations, establishment of usable performance baselines, and clear linkage between proposed improvements and measurable business outcomes.
3. Build Practical AI‑Assisted Automations and Reusable Analytical Solutions. develop and validate lightweight automations that improve the efficiency of assigned product analysis, reporting, data preparation, testing, or validation activities. Use approved AI‑assisted coding tools together with Python, SQL, APIs, or comparable technologies to create maintainable scripts and workflows that address documented business or delivery needs. Test generated code and outputs for accuracy, reliability, security, and appropriate handling of sensitive customer information before operational use. Success will be measured by successful execution of approved automations, reduction in manual effort against documented baselines, reproducibility of results, and acceptance of the solutions by their intended users.
1. Understand the Customer Servicing Environment and Identify Initial Opportunities. During the first 15–30 days, review the assigned digital servicing processes, customer chat journeys, operational reporting, existing automation capabilities, and relevant agentic AI initiatives. Meet with product, business, engineering, and architecture stakeholders to understand recurring customer problems, delivery constraints, available data, and current performance measures. Identify and document an initial set of customer friction points or automation opportunities, including the evidence available to support each one. Success will be measured by a validated understanding of the servicing environment, a documented opportunity inventory, and agreement on the initial use cases requiring deeper analysis.
2. Analyze Customer Interaction Data and Establish Performance Baselines. During the first 30–60 days, use SQL and approved analytical tools to investigate assigned customer experience problems and operational performance gaps. Analyze relevant interaction patterns, workflow outcomes, servicing exceptions, and available performance indicators to determine the size and likely causes of each problem. Document data assumptions, establish reproducible baseline measures, and present evidence‑based findings to product and business stakeholders. Success will be measured by validated analytical outputs, stakeholder acceptance of baseline calculations, and identification of actionable opportunities supported by data.
3. Develop Complete PRDs, Customer Journeys, and Acceptance Criteria. During the first 45–90 days, independently produce development‑ready Product Requirements Documents for prioritized customer servicing and agentic AI use cases. Translate business objectives and analytical findings into customer journeys, functional requirements, user stories, success metrics, dependencies, and testable acceptance criteria. For agentic workflows, explicitly document expected tool interactions, integration dependencies, fallback behavior, human handoff, and relevant failure scenarios. Success will be measured by approval of the PRDs, engineering readiness, completeness of requirements, and successful use of the documentation during implementation and testing.