Technical practice
Designing Large-Scale Forecasting Systems for Logistics
What to coverDiscuss how you would design a forecasting architecture for a global transportation network with high seasonality. Address feature engineering, handling missing data via SQL and data warehouses, and selecting between tree-based models and deep learning approaches like LSTMs.
Comparing LSTMs and Transformers for Time-Series Forecasting
What to coverExplain the trade-offs between recurrent neural networks like LSTMs and attention-based transformer models when modeling sequential shipment and demand data. Address computational complexity, training stability, and sequence length handling.
Applying Large Language Models to Logistics Operations
What to coverExamine how LLMs can be integrated into optimization or forecasting pipelines for supply chain decisions. Discuss prompt design, fine-tuning considerations, and how to handle inference latency constraints in production.
Reinforcement Learning in Transportation Optimization
What to coverDescribe how a reinforcement learning agent could be formulated for route optimization or resource allocation. Explain state representation, action space design, reward function formulation, and sample efficiency challenges.
Ensuring Production Scalability and Robustness for ML Models
What to coverOutline the steps you take to transition an experimental ML model into a robust, scalable production service. Discuss monitoring for data drift, handling fallback mechanisms, and optimizing inference performance.
Implementing Algorithms: Toolkits vs. Custom Code
What to coverWhen would you rely on standard machine learning libraries versus writing custom algorithm implementations from scratch? Discuss trade-offs in development time, optimization control, and maintainability.
Advanced SQL and RDBMS Query Optimization
What to coverGiven large-scale transactional transportation tables stored in an RDBMS like Oracle, how would you write and optimize complex SQL queries involving window functions and large joins to extract training features efficiently?
Translating Offline ML Metrics to Business Value
What to coverExplain how you bridge the gap between offline model metrics (such as RMSE or MAE) and end-to-end business metrics like cost optimization and network reliability. Discuss how to conduct cost-benefit analyses on model improvements.
Behavioural practice
Partnering with Cross-Functional Stakeholders
What to coverDescribe a situation where you had to collaborate closely with Product, Operations, or Engineering leaders to align on model requirements. Detail the situation, the actions you took to resolve competing priorities, and the resulting business impact using the STAR method.
Managing Production Incidents and Model Degradation
What to coverShare an experience where a deployed model or system faced unexpected performance degradation or scaling bottlenecks in production. Explain your diagnostic approach, the corrective actions implemented, and how you ensured long-term robustness.
Balancing Algorithmic Complexity with Timely Delivery
What to coverGive an example of a project where you evaluated multiple techniques ranging from simple tree-based models to complex deep learning or LLM architectures. Discuss how you weighed complexity against project timelines and business value.
Contributing to the Broader ML Community and Knowledge Sharing
What to coverDescribe a time when you shared technical insights, published findings, or contributed to internal knowledge sharing within your team or the broader machine learning community. Highlight your specific contribution and the outcome.