Key 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.
Requirements :
- 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.