People leadership & growth
Clear roles, specialization, onboarding, feedback, and an environment where people move from general response handling toward problem ownership and specialist knowledge.
My career has evolved from frontline user interaction and high-volume operations into team leadership, cross-functional operating design, and intelligent system building. I do not stop at reporting a problem; I turn it into process, data, tooling, ownership, and a learning loop so teams work more effectively, decisions become clearer, and organizational knowledge lasts.
A combination of human understanding, operational leadership, data-driven thinking, and the ability to build. Not merely a support manager and not merely an automation builder — I operate at the intersection.
Clear roles, specialization, onboarding, feedback, and an environment where people move from general response handling toward problem ownership and specialist knowledge.
Turning scattered user feedback into patterns, risks, priorities, management insight, and traceable action for relevant teams.
Identifying manual work and hidden friction, then turning them into workflows, dashboards, rules, and repeatable loops.
Using AI for analysis, development, learning, quality control, and reducing cognitive load — while keeping final judgment and accountability human.
My recurring pattern is to find the problem hidden inside everyday work, understand its root and impact, turn it into an executable solution, and then convert that solution into tooling or knowledge that does not depend on one person.
The journey runs from the earliest experience on the left to the current role on the right; arrow direction adapts automatically for English.
Leading team operations and quality, people development, Voice-of-User analysis and escalation loops, ongoing collaboration with product and other functions, and building dashboards, automations, knowledge assets, and decision-support reporting to make operations more scalable.
Multi-channel user support, issue diagnosis, early-stage software testing, and actionable feedback — a period in which my focus evolved from resolving requests to identifying patterns and collaborating more closely with product and testing.
Worked in a high-volume operational environment, processing large amounts of payment data and using financial panels — strengthening operational accuracy, sensitivity to data inconsistencies, and status control.
Developed skills in needs discovery, persuasion, communication, high-volume interaction management, and early team-performance supervision — a foundation that later shaped my approach to user operations and people development.
My earliest professional experience in sales and e-commerce — the beginning of practical exposure to customer behavior, communication, and outcome orientation.
Alongside my main organizational career path, I have also built and operated online specialist-translation services, marketing, customer communication, and digital assets.
My work at Badesaba has not been limited to behind-the-scenes operations. I have also contributed to audience-facing campaign events, product education content, and in-person brand presence.
Owned the end-to-end execution of live draw events for Badesaba's spin-the-wheel campaigns and payment services, including writing the run-of-show and host script, shaping the event flow, and hosting the live broadcast.
Contributed to product education content by providing voice-over for videos explaining Badesaba application features and how to use them.
Represented Badesaba at its booth for three consecutive days during the Khorasan Razavi Province Research & Technology Achievements Exhibition, held for Research & Technology Week.
Building capacity, not merely assigning tasks. Individual names and granular shares are intentionally omitted; the focus is on a transferable leadership model.
User support is not only a response channel. Real customer signals—from questions and suggestions to complaints, product issues and satisfaction—are captured and analyzed, then translated into actionable input for product, technical and commercial teams. The goal is to move support closer to an organizational decision-signal function.
Separating domains and creating specialist ownership instead of relying on general response handling — to deepen knowledge, speed decisions, and improve accountability.
Creating learning paths for new team members, using mentoring and ongoing feedback, and helping people move from task execution toward problem understanding.
Using data and reporting to replace vague impressions with a clearer view of capacity, focus, quality, and value-creating roles.
Turning experience, responses, decisions, and operational learning into reusable knowledge so team capability survives personnel changes.
A central part of my current professional identity is the deliberate use of AI to see better, build faster, and reduce operational friction — not merely using a collection of tools. I have moved AI from a personal tool into a layer of the operating system: from ticket/document analysis to workflow design, code generation, response auditing, reporting, documentation, and learning, with one constant principle: AI should free human capacity, not blindly replace accountability and judgment.
The first layer shows the problem and value; Extended mode also reveals the implementation and architecture of each case.
Designed a loop that turns user feedback from a passive record into an owned item with status, response, follow-up, and outcome re-check.
Architecture used a shared data model, bi-directional synchronization, queue/approval logic, dashboards, and n8n workflows. Internally it connected Voice of User with an operational function.
Built an operational layer over issue management so important changes, ownership, status, and required outputs are visible without constant manual review.
Used Jira REST/JQL, n8n, messaging alerts, duplicate-event prevention, HTML/Excel reporting, and output archiving. The goal was to turn the issue tracker from a technical repository into an operational input.
When an internal system lacks useful notifications or a ready API, the process becomes dependent on memory and repeated manual checks. I designed an independent monitoring and alert layer.
Used Network/XHR analysis, session and CAPTCHA handling, polling, state management, and n8n event notifications — demonstrating problem solving even without a documented API.
AI acts as a first-pass analyst rather than the final decision-maker: it identifies repetitive, ambiguous, or attention-worthy inputs while preserving final judgment for the expert.
Implemented with Gemini API and n8n, guardrail rules, outcome classification, and human-in-the-loop action. Design principle: when uncertain, the system should escalate to a human rather than take a risky action.
Built a source for viewing activity patterns, capacity, focus, and trends — moving performance conversations away from memory and subjective impressions.
Included activity-recording models, data-quality controls, period reporting, Google Sheets/Apps Script, and HTML dashboards. Detailed team data is intentionally excluded from the public version.
Turned daily experience, meeting decisions, and user patterns into reusable knowledge and reports that explain not only what happened, but what it means and what should happen next.
Built and developed a Confluence knowledge base, standardized data → analysis → recommendation → follow-up → re-check reporting, and used HTML/dashboard outputs for different audiences.
Documented numbers can live inside project detail, while the main portfolio should communicate how the work changed the way the organization operates.
Monitoring, collection, alerts, and recurring reporting are moved into systems so team energy can shift toward problem-solving and analysis.
Dashboards and decision-grade reporting turn scattered events into an understandable picture for management and partner teams.
Cross-functional processes move from sending messages and waiting toward visible status, ownership, responses, and follow-up loops.
Team learning, incidents, and decisions become documentation, standards, and reusable tools.
Tools serve the problem. The differentiator is the combination of managerial, analytical, and technical capability in one execution path.
Shahid Bahonar University of Kerman
Management planning courses, CCNA, CRM/Scrum training, One Million AI Prompters, and ongoing practical learning in AI and automation tools.