Objectives

We are looking for a Data Scientist with strong Machine Learning capability to design, validate, and productionize models that improve player experience and business outcomes across Amanotes games and data products.

This role will help Amanotes move from descriptive analytics to intelligent decisioning by turning user, product, and business data into ML-powered actions such as dynamic difficulty adjustment, recommendation, offer personalization, and ad optimization.

What You Will Do

Work on high-impact personalization and optimization use cases such as song recommendation, dynamic difficulty adjustment, ad frequency optimization, personalized IAP offers, churn prevention, and content or event performance prediction.
Build end-to-end ML workflows: problem framing, feature design, model development, offline validation, experiment design, online testing, monitoring, and iteration based on results.
Develop predictive models that support UA, Product, and business decisions, such as pUV/LTV prediction, early-value prediction, creative winning prediction, UA and portfolio ROI forecasting, and campaign or resource-allocation recommendation signals.
Partner closely with UA and Product teams to turn predictive outputs into practical workflows for planning, targeting, bidding, creative iteration, and growth decision-making.
Explore large-scale behavioral data to uncover player patterns, segments, and opportunities using statistical analysis, experimentation, and data science techniques.
Work at the intersection of Data Science, Product, Game Design, and Music Experience Design to improve how music changes player experience inside Amanotes products.
Contribute to ML-driven systems for game difficulty and player flow, including difficulty prediction, game skill estimation, frustration detection, and next-best difficulty or assist recommendations.
Support Amanotes music-experience initiatives by helping quantify, model, and validate how music mechanics, reactive audio, or musicalized gameplay affect retention, engagement, monetization, and user experience.
Work with Data Engineering to ensure the right data foundation, quality, and availability for model training, scoring, monitoring, and analysis.
Design and analyze A/B tests or other controlled experiments to measure model impact on engagement, retention, monetization, player experience, and product growth.
Communicate findings, trade-offs, and recommendations clearly to both technical and non-technical stakeholders.
Contribute to Amanotes broader data and AI capability by improving reusable data assets, experimentation practices, predictive systems, and data-science ways of working.

Qualifications

Bachelor's degree in Data Science, Statistics, Mathematics, Computer Science, Economics, or another quantitative field.
Strong foundation in statistics, experimentation, machine learning, and analytical problem solving.
Strong hands-on ability with SQL and Python for data extraction, analysis, feature engineering, and modeling.
Experience building data products or ML solutions end to end, from discovery and R&D to production or live experimentation.
Ability to work with messy, large-scale behavioral data and convert it into usable features, insights, and decisions.
Comfortable working cross-functionally with product, engineering, and business stakeholders in an ambiguous, fast-changing environment.
Clear communication in English and Vietnamese, with the ability to explain technical topics to non-technical audiences.

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.
Minimum 12 days of paid annual leave, plus 10 days of paid sick leave.
12 days working from home per year.

Consent Notice for Personal Data Processing

By applying to any position at Amanotes, you acknowledge and agree that your personal data will be collected and processed for recruitment-related purposes. Please read the Consent Notice for Personal Data Processing carefully before submitting your application.
Thank you for your interest in joining Amanotes.

About Amanotes

We are Amanotes – a dynamic music game company that's using cutting-edge technology to transform how people experience music.

Since 2014, we've led the global simple music game market with over 3.5 billion downloads across 190+ countries, including chart-topping titles like Magic Tiles 3, Tiles Hop, Dancing Road, and Duet Cats.

As the #1 Music Game Publisher worldwide and the #1 App Publisher from Southeast Asia by downloads, our ambition is clear: to create iconic, hybrid-casual music games that people love and play for years.

What truly sets Amanotes apart is not just what we build — it's how we grow and who we grow with:

We invest in your growth through learning budgets, coaching, and stretch opportunities.

We embrace a creative and dynamic culture, where everyone can play, share, and thrive.

We believe in work-life harmony — your rhythm matters here.

We recognize and reward impact, with competitive benefits and clear development paths.

Come join our music-filled workplace, where innovation meets rhythm — and let's create a magical music experience together.

Amanotes – Where Everyone Can Music.

How To Apply

Click the button or contact us at [email protected] .

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

Senior Data Scientist