Data Scientist (Hybrid) | Mandaluyong

Data Scientist

Location: Mandaluyong

Work Schedule: Monday to Friday; Hybrid (3 days RTO)

Role Overview The Data Scientist plays a technical and strategic role in building an ecosystem that delivers accurate, timely, and well-structured data for broader enterprise impact. This position focuses on delivering high-quality data and model solutions, supporting data governance foundations, and collaborating closely with internal technical teams (Product Leads, Machine Learning Engineers, and Software Developers) and external business units to enable use cases and champion a data-driven culture.

Key Responsibilities

• Use Case Enablement & Value Realization: Deliver timely and accurate data and model solutions by supporting data governance activities (such as mappings and data onboarding) and conducting data analyses that enable priority business use cases.

• Operational Excellence & Partnership: Uphold technical and documentation standards while collaborating effectively with internal data teams and external stakeholders.

• AI & Data Culture Leadership: Participate in activities that promote data literacy and AI awareness, such as brownbag sessions and knowledge-sharing initiatives, to foster a data-driven culture.

• Innovation & Personal Development: Explore techniques and tools to improve data governance and analytical efficiency, contribute to internal prototypes or automation efforts, and complete technical certifications or online courses to apply new learnings.

• End-to-End Lifecycle & Risk Management: Manage solution delivery from scoping and exploration through handover to stakeholders, proactively identifying risks to data/model integrity and spotting opportunities to enrich analyses with additional data assets.

Qualifications

• Bachelor's or Master's degree in Data Science, Statistics, Physics, Computer Science, Engineering, or a related field.

• Minimum of 2 years of experience in data science, data governance, or related roles.

• Proficiency in Python, SQL, and Git (familiarity with R is a plus).

• Strong proficiency in statistics, machine learning, and end-to-end automation, including the ability to leverage emerging technologies such as GenAI for analytical acceleration (e.g., semantic matching, data enrichment).

• Experience with exploratory data analysis, model building, business validation, and stakeholder management (experience in data cataloguing and/or data pipeline building is a plus).

• Ability to translate complex methods and results into clear, business-relevant insights with transparent communication of assumptions and caveats.

• Strong project management and scenario management skills, including structuring actionable steps, anticipating dependencies, estimating realistic timelines, and proactively addressing roadblocks or data gaps.