Project Manager (Based in HCMC)

OBJECTIVES

Two of Amanotes' most critical bets for the next three years sit inside the CEO Office: Personalization and AI Core.
• Personalization is about making every player's experience feel like it was built for them - adapting difficulty, song recommendations, and monetization to each individual user. This means Product Design, Data Engineering, and Data Science need to ship as one team, not three.
• AI Core is the foundation underneath everything: clean operational, user, and financial data that the whole company can actually trust and use to build agents.
  This role exists because both projects are too important to run without someone whose full-time job is making them move.

OUTCOMES

Personalization

Ship the Personalization feature pipeline on schedule by having Product Design, Data Engineering, and Data Science working off a single, agreed-upon roadmap, with no critical dependency left unresolved

Measurable uplift in user engagement (time spent, session length) and LTV in tested games

Reduce cross-team friction on Personalization to the point where the three disciplines can run a joint sprint without needing you in every room

AI Core

Establish a data quality standard for AI Core within the first 90 days: operational, user, and financial data are collected, clean, and auditable

Create an environment that helps everyone in Amanotes to build agents and applications based on the data

WHAT YOU WILL DO

At Amanotes, we work based on outcomes. The responsibilities below help clarify what this role covers day-to-day, but they're a guide, not a cage.

Personalization

Own the Personalization project plan end-to-end: timelines, milestones, dependencies, and the people accountable for each

Run the integration layer between Product Design, Data Engineering, and Data Science, surfacing conflicts early and keeping all three aligned on shared priorities

Develop a working understanding of the ML models powering personalization (dynamic difficulty adjustment, song recommendation, monetization optimization); understand how they are trained, what data they consume, and what their output means for the player experience

Drive the tuning and optimization cycle for personalization models: coordinate experiment design (A/B tests, bandit policies), track model performance metrics, surface insights from experiment results, and ensure learnings feed back into the next iteration of model parameters and feature engineering

Work with data scientists and vendor partnerships to translate model outputs into actionable product decision, bridging the gap between "the model says X" and "the product should do Y"

AI Core

Drive AI Core's data readiness agenda: work with data teams to define, track, and close gaps in data collection and data quality across all three data domains (operational, user, financial)

Continuously monitor the data usage to identify issues and take corrective actions. 

Shared

Prepare and present clear project status updates to CEO Office leadership - what's on track, what's at risk, and what needs a decision

Design and facilitate the collaboration structures (standups, reviews, retrospectives) that actually make cross-functional teams move faster

Document decisions and rationale so the institutional knowledge lives in the system, not in anyone's head

QUALIFICATIONS

Must Have

Run a complex cross-functional project end-to-end, involving at least two technical disciplines that don't naturally speak the same language

Enough data fluency to hold a credible conversation with data engineers about pipeline design and with data scientists about model readiness, training loops, and experiment interpretation

Enough product design fluency to hold a credible conversation with game designers and product owners about player experience trade-offs

Have worked on personalization, recommendation, or ML-powered product initiatives — you understand what it takes to move a model from prototype to production and how to measure whether it's actually working

A track record of building project visibility that leadership relies on, not because they're forced to, but because it's genuinely useful

Experience in agile methodologies and in a tech product company, where priorities shift and ambiguity is the default

Minimum 5 years of experience in project management, program management, or technical product management

Nice to Have

Experience with A/B testing frameworks, contextual bandits, or reinforcement learning in a product context

Familiarity with mobile gaming, music-tech, or entertainment products

Experience managing vendor/partner relationships for ML or data platform services

BENEFITS

At Amanotes, you will be enjoying the dynamic working environment with unique music culture many benefits as below:
Competitive salary upon experience
13th month salary
Year-end Bonus
Flexible working time
Personal learning and well-being budget
Team-building budget
Lunch and parking allowance
Various learning activities, including internal training & sharing, international conferences, and e-learning (Udemy, Linkedin Learning...).
Engaging music events: Music Night, Amasing Night, Music schools…
Employee Assistance Program to support mental health & well-being. 
12 days of paid annual leave, plus 10 days of paid sick leave.
12 days working from home per year.