Technical practice
Translating Ambiguous Business Problems into Analytical Frameworks
What to coverDescribe your process for taking a vague business challenge and defining it as a measurable analytical problem. Focus on how you identify key success metrics, determine the necessary data inputs, and align the proposed model with specific business outcomes.
Experimental Design and Causal Inference
What to coverExplain how you design experiments to isolate the impact of a specific intervention in a complex enterprise environment. Discuss your approach to hypothesis testing and how you distinguish correlation from causation when evaluating model performance.
Integrating ML Models into Enterprise Workflows
What to coverDiscuss the challenges of moving a model from a prototype to a production-ready system within an enterprise landscape like SAP. Address considerations for scalability, data quality, governance, and ensuring the solution remains robust over time.
Applying Agentic AI in Business Processes
What to coverHow do you approach the design and implementation of AI agents to automate or augment complex business workflows? Address the importance of responsible AI use, system integration, and maintaining human oversight in agentic contexts.
Model Selection and Trade-offs
What to coverWhen choosing between different ML techniques (e.g., XGBoost vs. deep learning models), how do you balance accuracy, interpretability, and computational cost? Provide an example of how you justified a specific model choice to stakeholders.
Handling Data Landscapes in SAP Environments
What to coverGiven the complexity of SAP data environments like S/4HANA or BW/4HANA, how do you approach data extraction and feature engineering? Suggestion: Focus on your experience with data governance and ensuring data reliability for downstream analytical models.
Anomaly Detection and Forecasting
What to coverDescribe your experience applying anomaly detection or forecasting techniques to business data. Explain how you validate these models to ensure they provide actionable intelligence rather than just noise.
Python and ML Framework Proficiency
What to coverDiscuss your experience using libraries like Scikit-learn, TensorFlow, or PyTorch for end-to-end ML pipelines. Highlight how you manage dependencies and ensure code reusability in a collaborative, team-based environment.
Behavioural practice
Influencing Senior Stakeholders
What to coverDescribe a situation where you had to influence a senior stakeholder to adopt a data-driven recommendation that conflicted with their initial intuition. How did you present your evidence and what was the final outcome?
Working in Embedded Customer Teams
What to coverProvide an example of a time you worked as an embedded expert within a customer's team. How did you balance the need to deliver technical results with the requirement to build strong, collaborative relationships with the client?
Managing Ambiguity in Projects
What to coverTell me about a project where the requirements were unclear or changed significantly mid-stream. How did you manage the project scope, communicate risks, and ensure the final deliverable still met the business objectives?
Proactive AI Adoption
What to coverGive an example of how you have proactively leveraged AI tools or techniques to improve your own daily workflows or the efficiency of your team. What was the impact on productivity or quality?