Senior Data Scientist

Skills

About the Role

You will design, validate, and productionize machine learning models that improve player experience and business outcomes across Amanotes games and data products. You'll work on high-impact use cases like song recommendation, dynamic difficulty adjustment, ad frequency optimization, personalized IAP offers, churn prevention, and content performance prediction. You will build end-to-end ML workflows covering problem framing, feature design, model development, offline validation, experiment design, online testing, monitoring, and iteration. You'll develop predictive models supporting UA, Product, and business decisions, partner closely with UA and Product teams, explore large-scale behavioral data, and work at the intersection of Data Science, Product, Game Design, and Music Experience Design. You'll also design and analyze A/B tests, communicate findings to stakeholders, and contribute to Amanotes' broader data and AI capability.

Requirements

• 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

Responsibilities

• Work on 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 including problem framing, feature design, model development, offline validation, experiment design, online testing, monitoring, and iteration
• Develop predictive models supporting UA, Product, and business decisions such as pUV/LTV prediction, early-value prediction, creative winning prediction, and UA/portfolio ROI forecasting
• Partner with UA and Product teams to turn predictive outputs into 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 and experimentation
• Work at the intersection of Data Science, Product, Game Design, and Music Experience Design to improve player experience
• Contribute to ML-driven systems for game difficulty and player flow, including difficulty prediction, skill estimation, frustration detection, and next-best difficulty recommendations
• Support music-experience initiatives by quantifying and modeling how music mechanics affect retention, engagement, monetization, and user experience
• Work with Data Engineering to ensure 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, and growth
• Communicate findings, trade-offs, and recommendations to technical and non-technical stakeholders
• Contribute to reusable data assets, experimentation practices, predictive systems, and data-science ways of working

Benefits

• 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