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This is a medium difficulty Machine Learning question, commonly asked during Technical Screen rounds at Citibank.

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This question is commonly asked for Data Scientist candidates at Citibank during technical interviews.

Handle missing values for LGD modeling | Citibank Interview Question

Quick Overview

This question evaluates a data scientist's understanding of missing-data mechanisms and imputation strategies in Loss Given Default (LGD) credit-risk modeling, including recognition of MCAR/MAR/MNAR assumptions, sources of bias and leakage, and validation approaches for performance and stability.

Handling Missing Values for LGD Modeling

Context

You are building a Loss Given Default (LGD) model using account- and borrower-level features captured around the time of default. The dataset contains both continuous and categorical variables with non-trivial missingness due to reporting gaps, system migrations, and process differences across products/regions.

Task

Describe how you would handle missing values in this LGD modeling context. Specifically, compare the following approaches:

Multiple imputation (e.g., MICE)
Model-based imputation (e.g., kNN, random forest, regression)
Business-rule fills (domain-driven heuristics)
Indicator variables (missingness flags; Unknown category)
Leaving missingness explicit (letting the model handle NA directly)

For each, explain:

Assumptions about the missingness mechanism (MCAR, MAR, MNAR)
Pros, cons, and typical use cases in LGD modeling
Guardrails to avoid bias and leakage
How you would validate the choice and measure impact on LGD performance and stability

Quick Overview

Context

Task

Describe how you would handle missing values in this LGD modeling context. Specifically, compare the following approaches:

Multiple imputation (e.g., MICE)

Model-based imputation (e.g., kNN, random forest, regression)

Business-rule fills (domain-driven heuristics)

Indicator variables (missingness flags; Unknown category)

Leaving missingness explicit (letting the model handle NA directly)

For each, explain:

Assumptions about the missingness mechanism (MCAR, MAR, MNAR)

Pros, cons, and typical use cases in LGD modeling

Guardrails to avoid bias and leakage

How you would validate the choice and measure impact on LGD performance and stability

Handle missing values for LGD modeling

Quick Overview

Handling Missing Values for LGD Modeling

Context

Task

Solution

Comments (0)

Handle missing values for LGD modeling

Quick Overview

Handling Missing Values for LGD Modeling

Context

Task

Solution

Comments (0)