Process Automation Engineer; AI​/ML Ops​/Data Science Vertical – Business Intelligence

Position: Process Automation Engineer (AI / ML Ops / Data Science Vertical) – Business Intelligence
Location: Manila

Department Business Intelligence and Data Analytics Level Experienced  (Individual Contributor)
Location Manila The  Business Intelligence and Data Analytics team plays a critical role in conducting close-loop data-driven business iterations. As business intelligence specialists and data analysts, our scope of work is not limited to just performance monitoring and reporting, but also in proactively finding actionable insights to help drive high-impact business changes. Our end-to-end data solution reduces the gap between the business teams and technical teams, and achieves real ‘intelligence’ in the iteration cycle.

Browse our Business Intelligence and Data Analytics team openings to see how you can make an impact with us.

About the Team:

Shopee PH is scaling intelligent systems across Operations, Commercial Team, SPX, Scommerce, etc. As we expand ML deployment in critical domains, we need an experienced Data Scientist who understands both statistical modeling and real-world operational nuance. This role is ideal for someone who thrives in ambiguity, understands the difference between data and domain truth, and is passionate about driving measurable impact through better feature engineering, normalization logic, and experiment design.

Job Description:

Lead feature strategy for ML and LLM projects

Work closely with business teams (Ops, SPX, Risk, SCommerce) to translate messy operational processes into structured model inputs

Own EDA, outlier detection, feature construction, and normalizations

Design robust logic to handle entity overlaps

Evaluate data leakage, overfitting risks, and data quality issues before ML engineers build pipelines

Partner with ML engineers to hand off model-ready datasets with clear logic

Contribute to experimentation plans and model evaluation metrics

Support knowledge documentation and AI governance compliance

Requirements:2–5 years experience in data science, machine learning, or quantitative analytics

Strong background in statistics, feature engineering, anomaly detection, and time series

Proficient in Python, SQL, Jupyter (bonus: Spark, Hive, PyTorch, or XGBoost)
Able to work in ambiguous, high-stakes environments (logistics, fraud, finance, risk, etc.)Strong communicator — can explain why a feature matters to business users and engineers

Familiarity with ML lifecycle, but comfortable focusing on upstream data/feature quality
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