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Analytics & Experimentation questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master analytics & experimentation interviews.

What difficulty level is this interview question?

This is a medium difficulty Analytics & Experimentation question, commonly asked during Onsite rounds at LinkedIn.

What role is this question designed for?

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

Identify Sales Professionals

Last updated: Mar 29, 2026

Quick Overview

This question evaluates a data scientist's competencies in feature engineering, ground-truth labeling and class imbalance handling, multimodal modeling of text, graph and behavioral signals, and rigorous evaluation for a classifier that identifies sales professionals on a professional networking platform.

Jul 12, 2025, 6:59 PM

Data Scientist

Onsite

Analytics & Experimentation

Classifying Sales Professionals on LinkedIn

Scenario

You are tasked with building a machine learning system that automatically classifies LinkedIn members who are likely to be sales professionals. The classifier will be used to power product features (e.g., Sales Solutions targeting, onboarding flows, and content recommendations) and analytics.

Questions

Features and Data Sources
- Which features would you engineer, and what data sources would you leverage to build the classifier?
Ground Truth and Class Imbalance
- How would you create labels (ground truth) for "sales professional" and handle class imbalance during training and evaluation?
Modeling Approaches
- Which modeling approaches would you start with and why? Include how you'd incorporate text, graph, and behavioral signals.
Evaluation Strategy
- How would you evaluate the model offline (validation) and online (experimentation), including metrics, splits, guardrails, and fairness checks?

Solution

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