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Day 126
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Ho Chi Minh City, Vietnam
Open 4 months, first seen by us 126 days agoEmployment type Full-time
Employment type in the employer’s posting: Full-timeFrom the employer’s posting
Build end-to-end ML workflows from problem framing through monitoring
Develop predictive models for LTV, creative performance, and UA ROI
Design and analyze A/B tests to measure model impact
Build ML systems for game difficulty and player flow
Partner with UA and Product teams to turn model outputs into workflows
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
Design and analyze A/B tests or other controlled experiments to measure model impact on engagement, retention, monetization, player experience, and product growth.
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.
Partner closely with UA and Product teams to turn predictive outputs into practical workflows for planning, targeting, bidding, creative iteration, and growth decision-making.
Bachelor’s degree in Data Science, Statistics, Mathematics, Computer Science, Economics, or another quantitative field.
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.
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.
From the employer’s posting.
What truly sets Amanotes apart is not just what we build — it’s how we grow and who we grow with:
In the employer’s words · Read in context
Open 4 monthsWe first saw this posting 126 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
Day 126
Up longer than most. Similar jobs usually come down between day 7 and day 56.
From 10,194 similar postings that came down. Coming down is not always a hire.
2 open jobs
Open jobs by the week we first saw them, last 12 weeks.
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- 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.
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New Data Scientist postings at the employers we follow are down 3% over the last 5 weeks compared with the 5 before.
Each bar is one week of new postings. The darkest bars are the last 5 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
We file this job under Data Scientist, by the work the posting describes.
Data scientists use statistics and models to answer harder questions, such as whether a change worked, what demand will look like next quarter, or which customers are likely to leave.
Postings usually ask for Python or R, SQL, statistics, experiment design and common machine learning methods.
Unlike an ML engineer, a data scientist's main output is often an analysis or a first model, not software running inside a product.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-2051.
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.
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.
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.
Full-time
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Ho Chi Minh City, Vietnam
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:
No clear work-authorization passage found. Eligibility is unconfirmed.