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

Predict job changes month by month | Other Interview Question

Quick Overview

This question evaluates competency in discrete-time survival modeling and end-to-end predictive system design, encompassing feature engineering, label/hazard formulation, leakage prevention, model evaluation, fairness assessment, and experimentation for production machine learning.

Predict Monthly Job-Change Risk (Discrete-Time Survival Setup)

Context

You are building a monthly model to predict the probability that a LinkedIn member will change jobs in a given month. The output will be used across member notifications, job-ad targeting, and recruiter tools.

Tasks

Features

Specify features across:
- Profile and job history (e.g., school, major, degree, normalized title/seniority, tenure in role/company, number of past moves/promotions, industry, company size, location/remote).
- Member behavior (e.g., recent profile edits, job searches and applications, views of job/company pages, job alerts, connections or messages with recruiters/HR, saved jobs, resume uploads, InMail activity, network growth, days active).
- Seasonality (e.g., month-of-year, graduation peaks, fiscal cycles, industry-specific hiring seasons).
- External demand (e.g., postings volume by title/geo, applicant-per-opening ratio, trend in postings, time-to-fill, unemployment rate, layoffs/news signals).

Label, data structure, and sampling

Define a hazard-style label with user–month rows: 1 if the user changes jobs in that month (and had not changed before), otherwise 0; censor rows after first change or inactivity.
Guard against leakage (e.g., ensure features only use data before the prediction month start; handle backfilled profile updates; exclude post-change behavior).
Address class imbalance and dataset size (e.g., downsample negatives with re-weighting or use focal loss; manage right-censoring).

Evaluation and fairness

Choose metrics: PR-AUC as primary, plus recall@fixed precision, calibration (Brier score, reliability curves), and time-split backtests (rolling-origin).
Include fairness checks across key groups (e.g., region, industry, seniority) for performance and calibration parity.

Product use and experimentation

Describe how predictions are used in:
- Member notifications.
- Job-ad targeting.
- Recruiter tools (candidate discovery/prioritization).
Propose an A/B testing plan to measure business impact and avoid feedback loops (e.g., shadow scoring, persistent holdouts, treatment indicators in training, cluster randomization, IPS/doubly-robust evaluation).

Quick Overview

Tasks

Features

Specify features across:

Profile and job history (e.g., school, major, degree, normalized title/seniority, tenure in role/company, number of past moves/promotions, industry, company size, location/remote).
Member behavior (e.g., recent profile edits, job searches and applications, views of job/company pages, job alerts, connections or messages with recruiters/HR, saved jobs, resume uploads, InMail activity, network growth, days active).
Seasonality (e.g., month-of-year, graduation peaks, fiscal cycles, industry-specific hiring seasons).
External demand (e.g., postings volume by title/geo, applicant-per-opening ratio, trend in postings, time-to-fill, unemployment rate, layoffs/news signals).

Label, data structure, and sampling

Define a hazard-style label with user–month rows: 1 if the user changes jobs in that month (and had not changed before), otherwise 0; censor rows after first change or inactivity.

Guard against leakage (e.g., ensure features only use data before the prediction month start; handle backfilled profile updates; exclude post-change behavior).

Address class imbalance and dataset size (e.g., downsample negatives with re-weighting or use focal loss; manage right-censoring).

Evaluation and fairness

Choose metrics: PR-AUC as primary, plus recall@fixed precision, calibration (Brier score, reliability curves), and time-split backtests (rolling-origin).

Include fairness checks across key groups (e.g., region, industry, seniority) for performance and calibration parity.

Product use and experimentation

Describe how predictions are used in:

Member notifications.
Job-ad targeting.
Recruiter tools (candidate discovery/prioritization).

Propose an A/B testing plan to measure business impact and avoid feedback loops (e.g., shadow scoring, persistent holdouts, treatment indicators in training, cluster randomization, IPS/doubly-robust evaluation).

Predict job changes month by month

Quick Overview

Predict Monthly Job-Change Risk (Discrete-Time Survival Setup)

Context

Tasks

Solution

Comments (0)

Predict job changes month by month

Quick Overview

Predict Monthly Job-Change Risk (Discrete-Time Survival Setup)

Context

Tasks

Solution

Comments (0)