Data Scientist — Modelling

Motolite Marketing Corp.; Quezon City; Vor Ort; PHP 700.000 - 1.300.000; Vollzeit

Zusammenfassung

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.

Qualifikationen

• 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.

Aufgaben

• 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.

Kenntnisse

Python

SQL

Time-series forecasting

Forecasting

Regression

Classification

Explainability

Databricks

MLflow

Ausbildung

Bachelor’s degree in quantitative field

Master’s degree preferred

Databricks ML Associate/Professional

Tools

Databricks

MLflow

Distributed computation

Jobbeschreibung

_ Join us and contribute to driving excellence at MOTOLITE! _

Job Summary:

The Data Scientist — Modelling develops and validates statistical and machine-learning models that improve prediction, diagnosis, optimization and business decisions. The role is accountable not only for model quality but for whether the model changes a decision and produces measurable value.

3. Key Duties and Responsibilities

Problem framing

• Frame modelling problems against a measurable business outcome and a specific operational decision point.
• Establish the baseline that the model must beat, and define what success and failure look like before development starts.
• Test new forecasting algorithms and machine-learning approaches against that baseline.

Model development

• Perform feature engineering, model development, validation, backtesting, explainability analysis and experiment design.
• Develop forecasting, quality, customer, maintenance, pricing and optimization models depending on assigned domain.
• Document assumptions, limitations, data requirements and failure modes.

Productionization and lifecycle

• Define model acceptance criteria, monitoring requirements, retraining triggers and retirement conditions.
• Work with data and AI engineers to integrate models into actual operational workflows rather than standalone notebooks.
• Monitor deployed model performance and drift, and act when performance degrades.

Value measurement

• Quantify incremental business value against the baseline after deployment.
• Report honestly where a model does not beat the baseline and recommend retirement.

4. Key Deliverables

• Production models with documented model cards.
• Evaluation and backtesting reports.
• Monitoring plans, drift reports and retraining schedules.
• Business-impact measurement against baseline.
• Reproducible experiment records and feature documentation.

5. Accountability and Success Measures

• Statistical validity and reproducibility of models.
• Explainability appropriate to the decision and the audience.
• Decision utility — whether the model changes what people do.
• Measured performance and value after deployment, not at development time.
• Honest reporting of model limitations.

6. Working Relationships

• Internal: Data Engineers; AI / LLM Engineers; BI & AI Context Engineers; Data & AI Translators; domain and plant experts; Data Science & AI Capability Head.
• External: academic or vendor partners for specialized methods, as directed.

7. Qualifications

Education

Bachelor’s degree in Statistics, Mathematics, Computer Science, Engineering, Economics or a related quantitative field. Master’s degree preferred for senior positions.

Experience

Three or more years developing and deploying statistical or machine-learning models with demonstrated production use. Manufacturing process, quality or demand-forecasting modelling experience preferred for site-assigned positions.

Certifications

Preferred: Databricks Machine Learning Associate or Professional. Optional: cloud machine-learning certification, specialized forecasting or reliability training.

8. Technical Skills

• Python, including the scientific and machine-learning stack; SQL.
• Time-series forecasting; regression and classification; clustering and segmentation.
• Statistical process control and quality analytics for manufacturing-assigned positions.
• Modelling on image-derived and sensor-derived features produced by vision and edge services.
• Optimization methods and experiment design.
• MLflow or equivalent for tracking, registry and reproducibility.
• Model explainability, validation and drift monitoring.
• Databricks and distributed computation.

9. Behavioural Competencies

• Scientific rigour and resistance to overfitting a narrative.
• Business framing — starts from the decision, not the algorithm.
• Clarity in explaining uncertainty to non-technical audiences.
• Willingness to kill a model that does not earn its keep.
• Collaboration with engineering to reach production.

10. Level Guidance

Data Scientist

Develops models on defined problems with technical supervision. Three or more years of relevant experience.

Senior Data Scientist

Owns a modelling domain, sets validation standards, mentors and reviews. Six or more years of relevant experience.

Lead Data Scientist

Sets modelling methodology and lifecycle standards enterprise-wide and deputizes for the Capability Head. Nine or more years of relevant experience.

“Motolite offers you not just a job, but a career with boundless opportunities”