Associate - Product Executive (AI Assistant)

This role exists to continuously improve the quality and real-world usefulness of Moni by running a disciplined evaluate–diagnose–improve loop across every conversation the assistant handles, helping Moni become a leading AI assistant in Vietnamese fintech and earn the trust of millions of Vietnamese merchants and users. Mô tả công việcAI Quality Evaluation: Own the evaluation system for Moni — build and maintain gold-set test datasets, run release-gate evaluations, and report quality against agreed thresholds (intent recognition, groundedness, deflection, bad-escalation) so that no regression reaches merchants. Failure Analysis Improvement Loop: Read and categorise real conversation logs every week, turn failure patterns into a prioritised fix list (prompt, knowledge base, intent design, hand-off logic), and drive each fix to a measurable quality gain within the improvement cycle. Knowledge Intent Coverage: Work with domain owners (tax, payment, lending, CS) to expand intent coverage and keep the knowledge base accurate, sourced and compliant — so the assistant answers more of what merchants actually ask, without inventing answers. Business Impact for Merchants Users: Define and track the product metrics that connect AI quality to business outcomes (AI adoption, task completion, deflection, retention), partner with the data team on dashboards, and translate what the numbers say into the next set of product bets. Customer Insight Pain Point Discovery: Run merchant interviews, surveys and log mining to uncover unmet needs, and convert them into product opportunities with a clear hypothesis and success measure. Cross-functional Delivery: Partner with AI engineering, design and operations to write clear requirements, join sprint planning, run UAT, and ship features on schedule with quality accepted before release. Yêu cầu công việcKnowledge / SkillsAnalytical rigour on unstructured data: can read hundreds of raw conversations and organise them into clean, non-overlapping failure categories that lead to an action — not just a summary. Data-driven decision making: comfortable working with dashboards, funnels and cohort data (SQL is a plus); defines a metric before proposing a solution. Product execution fundamentals: user flows, requirement specs, edge-case thinking, sprint delivery with an engineering team. AI literacy: understands how an LLM assistant works conceptually — prompting, knowledge base / RAG, evaluation — and can learn the depth on the job. Hands-on AI experience is not required. Clear communication (VN EN): can explain a technical quality problem to a business stakeholder and a business goal to an engineer. Working Style PersonalityQuality-obsessed: willing to read real user conversations daily and be uncomfortable with "good enough" answers. Fast learner in a moving field: the AI stack changes every few months; actively tracks what's new and tests it rather than waiting to be taught. Merchant-centric: genuinely curious about small business owners — meets merchants and frontline staff in person, not only through data. Evidence over opinion: brings data to disagreements, and changes their mind when the data says so. Ownership in ambiguity: comfortable building the process where none exists yet, and following through to the outcome. Education Experience (minimum)Bachelor's degree in Business, Economics, Engineering or a related field.1+ year in product, product operations, business analysis or data analysis. Outstanding fresh graduates with a strong analytical portfolio will be considered. Experience with agile delivery.