Production ML Scientist: Forecasting & Business Impact

Motolite Marketing Corp.; Quezon City; On-site; PHP 700,000 - 1,300,000; Full time

Job summary

Motolite Marketing Corp. is seeking a Data Scientist — Modelling to develop and validate statistical and machine‑learning models that drive prediction, diagnosis, optimization and better business decisions. The role emphasizes delivering measurable value, not just building models.

You will frame problems, design experiments, implement models and work with data and AI engineers to deploy solutions into real operations, monitoring performance and value over time.

Qualifications

• Bachelor’s degree in a quantitative field; Master’s preferred for senior roles.
• Three or more years developing and deploying statistical or ML models with production use.
• Manufacturing process, quality or demand-forecasting modelling experience preferred for site-assigned positions.
• Databricks ML certification or similar is a plus.

Responsibilities

• Frame modelling problems against measurable business outcomes and decisions.
• Perform feature engineering, model development, validation, backtesting and explainability analysis.
• Develop forecasting, quality, customer, maintenance, pricing and optimization models as assigned.
• Define model acceptance criteria, monitoring, retraining triggers and retirement conditions.
• Monitor deployed model performance and drift and intervene when needed.

Skills

Python

SQL

Time-series forecasting

Forecasting

Regression

Classification

Explainability

Databricks

MLflow

Education

Bachelor’s degree in quantitative field

Master’s degree preferred

Databricks ML Associate/Professional

Tools

Databricks

MLflow

Distributed computation

Job description

Motolite Marketing Corp. is seeking a Data Scientist — Modelling to develop and validate statistical and machine‑learning models that drive prediction, diagnosis, optimization and better business decisions. The role emphasizes delivering measurable value, not just building models.

You will frame problems, design experiments, implement models and work with data and AI engineers to deploy solutions into real operations, monitoring performance and value over time.