Process Automation Engineer (AI / ML Ops / Data Science Vertical) – Business Intelligence
Summary
Build and refine ML features for Shopee’s ecommerce systems, turning messy operational data into clean, model-ready inputs and collaborating with engineers and business teams.
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
