Data Scientist (Bulacan)

Philippine Batteries Incorporated; Bulacan; On-site; PHP 900,000 - 1,500,000; Full time

Job summary

Philippine Batteries Incorporated seeks a Data Scientist to convert manufacturing and enterprise data into predictive insights that boost efficiency, quality, and strategic decisions. You’ll explore data, test hypotheses, and experiment with ML, DL, and AI methods to drive continuous improvement across operations.

You will work on data preparation, modeling, visualization, and reporting, collaborating with data engineers and business teams to deliver actionable analytics and robust documentation.

Qualifications

• Proficient in statistical analysis, data modeling, and machine learning.
• Strong SQL skills for data querying, transformation, and performance optimization.
• Experienced in machine learning and deep learning frameworks for predictive and prescriptive modeling.
• Familiar with generative AI and agentic AI approaches for advanced analytics and intelligent automation.
• Knowledgeable in experimental design, hypothesis testing, and optimization methods for model development.
• Hands-on experience with cloud and big data platforms for scalable analytics.
• Skilled in data visualization and reporting tools for insight communication.
• Strong foundation in statistics, probability, and linear algebra to support advanced modeling.

Responsibilities

• Collect, clean, and prepare large datasets from manufacturing systems, enterprise platforms, and external sources to ensure quality and consistency.
• Perform exploratory data analysis, hypothesis testing, and statistical modeling to identify trends, correlations, and opportunities for improvement.
• Develop predictive and prescriptive models using machine learning, deep learning, and advanced statistical approaches.
• Apply feature engineering, dimensionality reduction, and optimization methods to improve model accuracy, efficiency, and interpretability.
• Experiment with and evaluate emerging AI approaches including generative and agentic AI for advanced simulation, anomaly detection, and decision support.
• Validate, benchmark, and refine models using appropriate performance metrics and cross-validation methods.
• Collaborate with data engineers, BI analysts, and cross-functional teams to align data requirements, ensure availability, and support analytics workflows.
• Create visualizations, dashboards, and reports to communicate insights and recommendations to technical and non-technical stakeholders.
• Document models, methods, workflows, and assumptions to ensure transparency, reproducibility, and knowledge sharing.
• Stay current on new tools, frameworks, and research in data science and AI, and assess their applicability to continuous improvement and data initiatives.
• Perform other related tasks as needed to support enterprise data initiatives and business objectives.

Skills

Statistical analysis

Data modeling

Machine learning

Hypothesis testing

Experimental design

Optimization methods

Big data platforms

Data visualization

Linear algebra

Programming concepts

Education

Graduate in Chemical Engineering, Industrial Engineering, Computer Science, Data Science, Applied Mathematics, Statistics

Tools

SQL

Cloud platforms

Big data platforms

Visualization tools

Job description

The primary function of the Data Scientist is to transform manufacturing and enterprise data into predictive and prescriptive insights that enhance efficiency, quality, and strategic decision-making. This role focuses on data exploration, hypothesis testing, and model experimentation, applying statistical methods, machine learning, deep learning, and emerging AI approaches including generative and agentic AI to drive continuous improvement across operation.

PRINCIPAL DUTIES

1. Collect, clean, and prepare large datasets from manufacturing systems, enterprise platforms, and external sources to ensure quality and consistency.
2. Perform exploratory data analysis, hypothesis testing, and statistical modeling to identify trends, correlations, and opportunities for improvement.
3. Develop predictive and prescriptive models using machine learning, deep learning, and advanced statistical approaches.
4. Apply feature engineering, dimensionality reduction, and optimization methods to improve model accuracy, efficiency, and interpretability.
5. Experiment with and evaluate emerging AI approaches including generative and agentic AI for advanced simulation, anomaly detection, and decision support.
6. Validate, benchmark, and refine models using appropriate performance metrics and cross-validation methods.
7. Collaborate with data engineers, BI analysts, and cross-functional teams to align data requirements, ensure availability, and support analytics workflows.
8. Create visualizations, dashboards, and reports to communicate insights and recommendations to technical and non-technical stakeholders.
9. Document models, methods, workflows, and assumptions to ensure transparency, reproducibility, and knowledge sharing.
10. Stay current on new tools, frameworks, and research in data science and AI, and assess their applicability to continuous improvement and data initiatives.
11. Perform other related tasks as needed to support enterprise data initiatives and business objectives.

JOB SPECIFICATIONS

1. Formal Education:

• Graduate of Chemical Engineering, Industrial Engineering, Computer Science, Data Science, Applied Mathematics, Statistics, or other closely related analytical fields with a strong foundation in quantitative methods, programming, and data analytics; Latin honors preferred.

2. Experience:

• 2-3 years of professional experience in data science, analytics, or applied statistics.

3. Training/Skills:

• Proficient in statistical analysis, data modeling, and machine learning.
• Strong SQL skills for data querying, transformation, and performance optimization.
• Experienced in machine learning and deep learning frameworks for predictive and prescriptive modeling.
• Familiar with generative AI and agentic AI approaches for advanced analytics and intelligent automation.
• Knowledgeable in experimental design, hypothesis testing, and optimization methods for model development.
• Hands-on experience with cloud and big data platforms for scalable analytics.
• Skilled in data visualization and reporting tools for insight communication.
• Strong foundation in statistics, probability, and linear algebra to support advanced modeling.