Responsibilities
- Develop, train, and optimize machine learning models for demand–supply prediction, route optimization, and customer/driver churn analysis.
- Implement predictive pipelines using structured and unstructured transportation data (GPS, sensor, transactional, behavioral).
- Collaborate with product and engineering teams to integrate ML models into production systems.
- Conduct exploratory data analysis (EDA) to identify key features, correlations, and anomalies in transportation datasets.
- Apply machine learning frameworks (e.g., scikit-learn, TensorFlow, PyTorch) to prototype and deploy AI models.
- Perform model validation, testing, and monitoring to ensure reliability and scalability in real‑world use cases.
- Support data engineers in building scalable data pipelines and maintaining data quality.
- Document model design, performance metrics, and deployment processes for team knowledge sharing.
- Bachelor’s degree in Computer Science, Data Science, Statistics, or related field.
- 3–5 years of experience in applied data science or machine learning engineering.
- Solid knowledge of supervised/unsupervised learning, regression, classification, clustering, and time‑series forecasting.
- Hands‑on experience in Python (pandas, scikit‑learn, TensorFlow/PyTorch), SQL, and cloud environments (GCP/AWS/Azure).
- Familiarity with MLOps practices (CI/CD for ML, model versioning, monitoring).
- Strong problem‑solving skills with ability to handle large‑scale, imbalanced transportation datasets.
- Good communication skills to collaborate with cross‑functional teams.
Jakarta, Indonesia
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