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.