Process Automation Engineer (AI / ML Ops / Data Science Vertical) – Business Intelligence

Shopee · Commerce · Python · SQL · 2 months ago

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Location

In office (Manila)

Seniority

Junior · 2+ years exp

Confirmed on the employer's own hiring board on Nov 12, 2026. First seen by Alion on Sep 24, 2026. Shopee scores A on the Alion truth index.

Overview

Company

Impact

Profile match

Shopee

Shopee is a multinational technology company and e-commerce marketplace platform operating as a subsidiary of Sea Limited. The company provides a hybrid B2C and C2C digital marketplace offering retail products, integrated digital payments and financial services via ShopeePay/SeaMoney, and end-to-end logistics and fulfillment solutions through SPX Express. Headquartered in Singapore, the platform serves millions of consumers and merchant partners across Southeast Asia, Taiwan, and Latin America.

shopee.com · HQ: Singapore · Marketplaces · 5000+ employees · Est. 2015

Tech stack of Shopee

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

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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