AI & Analytics Manager: Forecasting, Growth & MLOps

Universal Robina Corp; Philippines; On-site; PHP 1,800,000 - 2,400,000; Full time

Job summary

Universal Robina Corporation is seeking an Applied Analytics and AI Manager to lead data-driven decision-making by building analytical solutions for growth and efficiency. You will collaborate with cross-functional teams to identify opportunities and deploy AI/ML models in a modern data ecosystem.

You will productionize models on Databricks with MLflow, frame problems, run experiments, and champion MLOps, helping the organization maximize value from data assets.

Qualifications

• Master's degree or PhD in Data Science, Statistics, Computer Science, Economics, or related field.
• 5+ years of experience in data science, machine learning, or advanced analytics.
• Strong proficiency in Python, SQL, machine learning, and statistical modeling.
• Experience with Databricks, MLflow, PySpark, and AI/ML model deployment.
• Strong communication and stakeholder management skills.
• FMCG, retail, or consumer goods industry experience is an advantage.

Responsibilities

• Develop, validate & deploy AI and ML models for demand forecasting, price/promo, market-share and ordering.
• Productionize models on Databricks with MLflow; partner with engineers on feature pipelines.
• Frame business problems with BUs and quantify value; design & run experiments (A/B testing).
• Power Command Center predictive features and the smart-ordering use cases (2027-28 roadmap).
• Champion MLOps: model registry, monitoring, drift detection and retraining.

Skills

Python

SQL

Machine Learning

Statistical Modeling

Communication

Cross-functional collaboration

Data storytelling

Education

Master's or PhD (Data Science/Statistics/CS/Economics)

Tools

Databricks

MLflow

PySpark

Job description

Universal Robina Corporation is seeking an Applied Analytics and AI Manager to lead data-driven decision-making by building analytical solutions for growth and efficiency. You will collaborate with cross-functional teams to identify opportunities and deploy AI/ML models in a modern data ecosystem.

You will productionize models on Databricks with MLflow, frame problems, run experiments, and champion MLOps, helping the organization maximize value from data assets.