Jr. Data Scientist
Smart (PLDT Group) Makati City, National Capital District, Philippines
Telecommunications · 5,001-10,000 employees
About the role
Collaborate with engineering teams to develop, optimize, and deploy machine learning models across cloud and on-premises environments. Oversee the full model lifecycle, including data preparation, feature engineering, monitoring, and troubleshooting.
What they look for
Requirements
Requires a bachelor's degree in a quantitative discipline and 2-4 years of experience with the full ML/AI lifecycle. Candidates must be proficient in Python, SQL, and cloud environments, with exposure to Generative AI and responsible AI practices.
Full description
Education
Bachelor’s degree holder of any quantitative discipline such as Data Science, Statistics, Computer Science, or related quantitative field
Qualifications
- 2–4+ years’ experience with the full ML/AI lifecycle from data preparation to deployment and monitoring
- Strong proficiency in Python, SQL, and experience with AWS/Databricks environments
- Good communication skills to explain complex ML/AI concepts to diverse stakeholders
- Exposure to Generative AI or LLMs, with understanding of responsible AI practices (bias, fairness, data privacy)
- Hands-on experience with LLMOps, RAG, or Agentic AI
- Version Control (git) and experience with CI/CD pipelines for ML model deployment
Duties and Responsibilities
- Collaborate with data science and engineering teams to develop, optimize, and deploy machine learning models in on premises and cloud environments.
- Oversee the ML model lifecycle from data preparation and feature engineering to deployment and monitoring. • Prepare, clean, and transform data to be used for training and inference.
- Ensure the scalability, reliability, and performance of machine learning models.
- Monitor and maintain deployed models, implement updates, and troubleshoot any issues that may arise.
- Assist in designing and maintaining machine learning pipelines, ensuring efficient data flow and model deployment.
- Translate analytical findings into clear recommendations for stakeholders.
- Ensure responsible AI practices are followed, addressing bias, fairness, and ethical considerations in line with Data Privacy, Cybersecurity, and AI policies.
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