Technical practice
Designing Scalable Pricing Optimization Models
What to coverExplain how you would approach a pricing optimization problem for a telecommunications product. Discuss the trade-offs between using traditional statistical models like GLMs versus more complex machine learning approaches like gradient boosted trees, and how you would validate the model's impact on revenue.
Anomaly Detection in Large-Scale Network Data
What to coverDescribe your methodology for detecting anomalies in high-velocity network data. Focus on how you handle seasonality, noise, and the requirement for real-time processing, and suggest how you would perform root cause analysis once an anomaly is identified.
Operationalizing Generative AI and LLMs
What to coverDiscuss the lifecycle management of LLMs in an enterprise environment. Address how you would approach fine-tuning, prompt engineering, and red-teaming to ensure model safety, performance, and alignment with specific business use cases.
Implementing Digital Twins for Scenario Testing
What to coverHow would you architect a digital twin to simulate operational or network scenarios? Explain the data requirements, the choice of simulation techniques, and how you ensure the digital twin accurately reflects real-world system behavior.
ML Lifecycle and Governance
What to coverDescribe your experience with MLOps tools like MLflow and Airflow for managing model versions and pipeline orchestration. Explain how you ensure model governance, reproducibility, and monitoring in a production environment.
Causal Inference in A/B Testing
What to coverWhen conducting A/B tests for marketing or pricing strategies, how do you distinguish correlation from causation? Explain your approach to designing experiments and using causal inference techniques to isolate the impact of specific business levers.
Data Engineering for AI Pipelines
What to coverGiven the need to work with Spark, SQL, and Databricks, describe how you optimize data pipelines for large-scale datasets. Focus on strategies for efficient data partitioning, handling distributed computing challenges, and ensuring data quality.
Software Engineering Best Practices in Data Science
What to coverAs a senior member, how do you enforce modular code design and version control standards within a team? Explain your approach to conducting code reviews and ensuring that research-oriented code is production-ready.
Behavioural practice
Mentoring and Technical Leadership
What to coverDescribe a situation where you mentored a junior team member through a complex technical challenge. Focus on how you balanced providing guidance with allowing them to solve the problem independently, and the impact on their growth.
Translating Business Challenges into AI Solutions
What to coverProvide an example of a time you worked with non-technical stakeholders to define a business problem. Explain how you translated their requirements into a technical roadmap and how you managed expectations throughout the project lifecycle.
Handling Cross-Functional Conflict
What to coverTell me about a time you had to align different departments (e.g., Network, Finance, or Product) on an AI initiative. How did you navigate competing priorities and ensure the final solution met the needs of all stakeholders?
Navigating Project Failure or Model Underperformance
What to coverDescribe a scenario where an AI model or project did not deliver the expected results. How did you perform the root cause analysis, communicate the findings to stakeholders, and what steps did you take to pivot or improve the outcome?