Decision Science Test

Why this matters?

Understanding the distinction between these two categories of algorithms is fundamental for a decision scientist, as it informs the approach they take when developing predictive models.

What to listen for?

A good candidate should be able to explain the basic concepts, give examples of each type of algorithm, and discuss when one might be preferred over the other.

Why this matters?

This question assesses a candidate's understanding of the end-to-end modeling process, including data preparation, feature engineering, model selection, evaluation, and deployment.

What to listen for?

A strong candidate should be able to walk through the steps of the process and explain the reasoning behind each step. They should also be familiar with common tools and techniques used in each stage.

Why this matters?

Overfitting occurs when a model fits the training data too well and doesn't generalize well to new data. It's a common problem in predictive modeling and preventing it is critical for developing accurate models.

What to listen for?

A candidate should be able to define overfitting, explain its causes, and provide examples of techniques to prevent it, such as regularization, cross-validation, or early stopping.

Why this matters?

Feature selection is the process of choosing the most relevant variables to include in a model. It's an important step in the modeling process, as it can have a big impact on model performance.

What to listen for?

A good candidate should be able to explain different feature selection methods, such as backward selection, forward selection, or wrapper methods. They should also be able to discuss the trade-offs between different methods and when one might be preferred over the others.

Why this matters?

A/B testing is a common technique used by decision scientists to determine the effectiveness of different alternatives. It's an important tool for making data-driven decisions and measuring the impact of interventions.

What to listen for?

A strong candidate should be able to describe a real-world example of A/B testing they've performed, including the design of the experiment, the results, and the decisions made based on the results. They should also be able to discuss the limitations of A/B testing and the assumptions it makes.