S
Sea
MLOps
Process Automation Engineer (AI / ML Ops / Data Science Vertical) – Business Intelligence
On-siteMidMLOpsposted 3mo ago
Role summaryAI-generated
Lead feature strategy for ML and LLM projects, working closely with business teams to translate operational processes into structured model inputs, with a focus on data quality and model evaluation.
Skills required
About this role
- 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
