Hybrid Data Scientist - Automations, ML & Analytics Lead

Schneider Electric; Philippines; Hybrid; PHP 1,000,000 - 2,000,000; Full time

Job summary

Schneider Electric is seeking a Data Scientist to perform deep-dive analytics, develop predictive models, and drive automated improvements across business units. The role emphasizes end-to-end data solutions, engagement with diverse stakeholders, and leadership on high-visibility automation projects.

You will build and validate data models, apply machine learning techniques, and document processes for cross-training and scalability.

Qualifications

• 3 years analytics experience and familiarity with project lifecycle concepts.
• Extensive programming in SQL, PL/SQL, Tableau, Python, R, Visual Basic and other languages.
• Experience with AWS cloud and data architectures (relational and non-relational databases).
• Knowledge of machine learning techniques (clustering, decision trees, neural networks) and statistics.
• Strong communication and collaboration across teams; ability to influence stakeholders.

Responsibilities

• Mine and analyze data to drive optimization of product development and marketing strategies.
• Develop and deploy predictive models; drive business outcomes with data insights.
• Coordinate with cross-functional teams to implement models and monitor outcomes.
• Create processes to monitor model performance and data accuracy.
• Lead automation of data flows and analytics; develop training modules for rollout.
• Provide business information and analysis to support decision making.

Skills

SQL

Python

Tableau

R

Machine Learning

AWS

Data Modeling

Education

Engineering/CS/Statistics degree

Tools

AWS

SQL

Python

Job description

Schneider Electric is seeking a Data Scientist to perform deep-dive analytics, develop predictive models, and drive automated improvements across business units. The role emphasizes end-to-end data solutions, engagement with diverse stakeholders, and leadership on high-visibility automation projects.

You will build and validate data models, apply machine learning techniques, and document processes for cross-training and scalability.