Applied Machine Learning Manager

Newport World Resorts; Pasay; On-site; PHP 2,000,000 - 2,800,000; Full time

Job summary

Newport World Resorts is seeking an Applied ML Manager to lead the design, development, and deployment of production-ready ML models powering personalization, segmentation, prediction, and automated decisioning across core product domains. You will partner with Product, Data & Analytics, Engineering, and business teams to translate models into measurable business outcomes.

Responsibilities span ML strategy, model development, deployment, MLOps, and governance, with a focus on ethical standards,

Qualifications

• Bachelor's or Master's degree in Computer Science, Statistics, Engineering, Mathematics, Data Science, or related field.
• 6-10 years of experience in applied machine learning, data science, or related roles.
• Experience managing or mentoring ML/DS teams.
• Experience deploying ML models into production environments.
• Experience collaborating with cross-functional technical and non-technical teams.
• Experience in digital products, CRM/MarTech, gaming, hospitality or consumer tech is an advantage.
• Strong background in supervised/unsupervised learning, time-series, recommendation systems, and optimization.
• Proficiency with ML frameworks, model evaluation, experimentation, and feature engineering.
• Understanding of pipelines, automation, and MLOps concepts.
• Strong analytical and problem-solving skills.
• Ability to translate business objectives into ML solutions and communicate complex concepts clearly.

Responsibilities

• Define the short- and long-term ML roadmap aligned to priorities.
• Prioritize use cases based on impact, feasibility, data readiness, and ROI potential.
• Champion ML capabilities for personalization, churn prevention, revenue optimization, and operational efficiency.
• Lead development of ML models: segmentation & clustering, propensity modeling, forecasting & demand prediction, recommendation systems, anomaly detection, marketing uplift & optimization models.
• Guide the team through data exploration, feature engineering, model training, validation, and refinement.
• Conduct A/B testing, controlled experiments, and uplift model validation.
• Work with Engineering to deploy models into production systems.
• Define monitoring, drift detection, retraining policies, and model performance dashboards.
• Establish best practices for versioning, documentation, reproducibility, and automated retraining.
• Work with Product Managers to define ML requirements, model objectives, and KPIs.
• Partner with D&A on data pipelines, transformations, and feature availability.
• Collaborate with business teams on model activation and embedding ML insights into workflows.
• Communicate model performance, risks, and insights to senior stakeholders.
• Ensure models meet ethical, regulatory, and privacy standards.
• Maintain rigorous validation, documentation, and review processes.
• Identify emerging ML tools, trends, and methodologies to improve productivity and accuracy.

Skills

Mentoring ML/DS teams

Production ML deployment

Cross-functional collaboration

Model evaluation & experimentation

Feature engineering

ML frameworks proficiency

Data science problem solving

Analytical reasoning

Translating business objectives to ML

Ethical/regulatory awareness in ML

Education

Bachelor's or Master's in CS/Stats/Engineering/Data Science

Job description

JOB SUMMARY

The Applied Machine Learning (ML) Manager leads the design, development, and operationalization of ML models that power personalization, segmentation, prediction, and automated decisioning across the core product domains: Customer Acquisition, Marketing Engagement, Service Operations, and Data Enablement.

This role manages a small high-impact applied ML function responsible for building production-ready models (classification, forecasting, recommendation, optimization), deploying them into digital products, and partnering closely with Product Managers, Data & Analytics (D&A), Engineering and business teams to translate models into measurable business outcomes.

The ML Manager is responsible for establishing MLOps standards, model monitoring practices, and the end-to-end ML lifecycle from feature engineering to deployment.

RESPONSIBILITIES

ML Strategy & Roadmap

• Define the short- and long-term ML roadmap aligned to priorities.




• Prioritize use cases based on impact, feasibility, data readiness, and ROI potential.




• Champion ML capabilities for personalization, churn prevention, revenue optimization, and operational efficiency.

Model Development & Experimentation

• Lead development of ML models:

  
  
  
  

  • segmentation & clustering

  
  

  • propensity modeling

  
  

  • forecasting & demand prediction

  
  

  • recommendation systems

  
  

  • anomaly detection

  
  

  • marketing uplift & optimization models

  
  
  
  



• Guide the team through data exploration, feature engineering, model training, validation, and refinement.




• Conduct A/B testing, controlled experiments, and uplift model validation.

ML Deployment & MLOps

• Work with Engineering to deploy models into production systems.




• Define monitoring, drift detection, retraining policies, and model performance dashboards.




• Establish best practices for versioning, documentation, reproducibility, and automated retraining.

Cross-Functional Collaboration

• Work with Product Managers to define ML requirements, model objectives, and KPIs.




• Partner with D&A on data pipelines, transformations, and feature availability.




• Collaborate with business teams on model activation and embedding ML insights into workflows.




• Communicate model performance, risks, and insights to senior stakeholders.

Governance, Quality & Continuous Improvement

• Ensure models meet ethical, regulatory, and privacy standards.




• Maintain rigorous validation, documentation, and review processes.




• Identify emerging ML tools, trends, and methodologies to improve productivity and accuracy.

QUALIFICATIONS

• Bachelor's or Master's degree in Computer Science, Statistics, Engineering, Mathematics, Data Science, or related field.




• At least 6-10 years of experience in applied machine learning, data science, or related roles.




• Experience managing or mentoring ML/DS teams.




• Experience deploying ML models into production environments.




• Experience collaborating with cross-functional technical and non-technical teams.




• Experience in digital products, CRM/MarTech, gaming, hospitality or consumer tech is an advantage.

• Strong background in supervised/unsupervised learning, time-series, recommendation systems, and optimization.




• Proficiency with ML frameworks, model evaluation, experimentation, and feature engineering.




• Understanding of pipelines, automation, and MLOps concepts.




• Strong analytical and problem-solving skills.




• Ability to translate business objectives into ML solutions and communicate complex concepts clearly.