ML Interview Q Series: Can you write a query to test if frequent job changers reach data science manager roles faster?
📚 Browse the full ML Interview series here.
Comprehensive Explanation
One way to approach this question is to calculate the time it takes for each individual to get promoted to a manager role (data science manager) from the time they first hold a data scientist position, then compare it against how many times that person has switched jobs. We can structure our steps as follows:
Defining Time to Manager
For each person i, define T_i as the duration between the start date of their first data scientist role and the start date of their first manager role. Symbolically, we might write:
Where T_i is measured in days, months, or years (depending on your data format). We want to see if T_i is generally shorter for those who switch jobs more often.
Counting Job Switches
We can define S_i (in plain text) as the total number of times user i changes jobs while still within the data-science-related track (data scientist, senior data scientist, or data science manager). For instance, a user might have the following sequence of titles in the user_experiences table:
Data Scientist at Company A
Senior Data Scientist at Company B
Data Scientist at Company C
Data Science Manager at Company C
Here, if we track transitions (A->B, B->C) plus the change in job title that eventually leads to a manager role, we can compute how many times the user switched employers or had major role changes before reaching manager.
Structuring the Data
You might have a user_experiences table structured like this:
user_id
job_title
start_date
end_date
Assume the roles of interest are "Data Scientist," "Senior Data Scientist," and "Data Science Manager." We want to figure out:
The first time the user holds a "Data Scientist" or "Senior Data Scientist" title (whichever qualifies as their earliest data science role in your data).
The time they first become a "Data Science Manager."
How many total job transitions occur in the user’s data science career before reaching "Data Science Manager," if at all.
Example High-Level SQL Logic
A rough sketch in Python (simulating the SQL) might look like this:
import sqlite3
# Connect to a sample database (in-memory here for demonstration)
conn = sqlite3.connect(':memory:')
cursor = conn.cursor()
# Suppose we already have a user_experiences table
# with columns: user_id, job_title, start_date, end_date
# We'll illustrate the type of query logic we might do.
query = """
WITH ds_first_role AS (
SELECT
user_id,
MIN(start_date) AS first_ds_start
FROM user_experiences
WHERE job_title IN ('Data Scientist', 'Senior Data Scientist')
GROUP BY user_id
),
manager_start AS (
SELECT
user_id,
MIN(start_date) AS manager_start
FROM user_experiences
WHERE job_title = 'Data Science Manager'
GROUP BY user_id
),
job_switches AS (
SELECT
user_id,
-- Count distinct transitions between consecutive roles or companies
COUNT(*) AS num_switches
FROM (
SELECT
user_id,
-- Identify each change in job_title or company_id (if you have company_id)
LAG(job_title) OVER (PARTITION BY user_id ORDER BY start_date) AS prev_title,
job_title AS current_title
FROM user_experiences
WHERE job_title IN ('Data Scientist', 'Senior Data Scientist', 'Data Science Manager')
)
WHERE current_title != prev_title
GROUP BY user_id
)
SELECT
f.user_id,
(m.manager_start - f.first_ds_start) AS time_to_manager,
j.num_switches
FROM ds_first_role f
JOIN manager_start m ON f.user_id = m.user_id
LEFT JOIN job_switches j ON f.user_id = j.user_id
WHERE time_to_manager IS NOT NULL
"""
cursor.execute(query)
results = cursor.fetchall()
# results might look like:
# [(user_id, time_to_manager, num_switches), ...]
# From here, we can segment the population by number of job switches
# and compare average time_to_manager.
In practice, you would:
Segment users into those with a higher-than-median number of switches vs. those with a lower-than-median number of switches.
Calculate the average time_to_manager for each group.
Compare these averages to see if frequent switching correlates with a faster (lower average) time to manager.
Statistical Test
To conclusively determine if the difference between the two groups (frequent switchers vs. non-frequent switchers) is statistically significant, you could conduct a hypothesis test, such as a two-sample t-test, to compare the average T_i values between these two cohorts. While the question focuses on writing a query, a full analysis would go further and conduct a statistical test on the results of that query.
For instance, if we define:
bar{T}_{low_switch} is the average time to manager for users who switched jobs less often.
bar{T}_{high_switch} is the average time to manager for those with frequent switches.
A positive \Delta that is statistically significant suggests frequent switchers reach manager more quickly (since high_switch group has a smaller time to manager). A negative \Delta suggests the opposite. Whether \Delta is meaningfully different from zero is where the test comes in.
Potential Follow-Up Questions
If the Data Has Missing or Incomplete Records, How Would You Handle That?
In many real-world scenarios, the user_experiences table might be incomplete. For example, some users may never go on to hold a data science manager title, or their employment records might not include exact start dates. In such cases:
You may need to filter out or impute the missing data (e.g., ignoring records with no manager role data if the question strictly requires the time to manager).
You could introduce censoring if you treat the problem as a survival analysis, where some individuals never experience the “manager” event within the observed timeframe.
What if Users Jump into Manager Roles from Non-Data-Science Positions?
Some individuals might move into a data science manager role directly from a role like “Software Engineer” or “ML Engineer.” To account for these transitions, you could either:
Expand the role buckets considered relevant.
Strictly define that only “Data Scientist” and “Senior Data Scientist” feed into a potential “Data Science Manager” promotion path, and disregard transitions from unrelated positions.
How Would You Account for Different Company Sizes or Different Promotion Criteria?
Promotion velocities might vary drastically between startups and large corporations. You could:
Include company type or size in your query as an additional grouping factor.
Compare time_to_manager within subsets of companies with similar characteristics.
Could You Use Another Analytical Method Instead of a Simple Query?
Yes, you might use time-to-event analysis, such as a Cox Proportional Hazards model, to capture how the risk (hazard) of promotion to manager changes over time with respect to the number of switches. This allows a more nuanced analysis that handles censored data (people who have not yet become managers) and simultaneously includes other covariates.
Would a Linear Regression Make Sense Here?
One might perform a linear regression where the dependent variable is time_to_manager. However, time-to-manager could be skewed (not normally distributed). Survival analysis is typically more appropriate for “time to event” data, but if you only want a rough correlation measure, linear regression could serve as a baseline.
How Could We Validate the Results Further?
After computing the difference in average time to manager:
Perform significance tests (e.g., t-test, ANOVA, etc.).
Visualize distributions via box plots or KDE plots to see how job switches affect the distribution of time_to_manager.
Consider confounding variables like education, skill level, or region, which might also influence promotion speed.
By addressing these aspects, you gain confidence in whether frequent job hopping truly explains faster promotions or if other factors are more influential.
Below are additional follow-up questions
How Do We Handle Situations Where an Employee Experiences a Demotion or a Lateral Move Rather Than a Promotion?
A demotion or lateral move poses an edge case because our initial analysis focuses on the time it takes to reach a “manager” role. If a user experiences a shift from data scientist to a non-manager role that doesn’t advance them forward, it can create ambiguity in interpreting job changes:
Data Cleanup: You might exclude demotions or lateral moves altogether if they are rare in your dataset, or label them separately so they’re not lumped in with upward transitions.
Trajectory Analysis: You could model a user’s career trajectory as a state machine, where roles like “Data Scientist,” “Senior Data Scientist,” and “Manager” are forward steps, while other role changes might be neutral or backward transitions. Such a model helps you determine how many times an individual moves forward vs. sideways or backward.
Edge Cases: Employees could move from “Senior Data Scientist” to “Data Scientist” again (perhaps a different company with a different title convention). This complicates “time to manager” because it might extend or shorten the perceived timeline artificially. You need a consistent rule set for deciding whether to pause or reset the timer in such scenarios.
Can External Economic or Market Factors Affect Promotion Timelines?
External influences such as economic downturns, hiring freezes, or widespread layoffs can alter promotion patterns:
Macro-Level Variables: Incorporate variables like year-over-year hiring trends, overall job market stability, or industry-wide layoffs. These might be gleaned from external data sources (e.g., labor statistics or job market reports).
Temporal Filtering: Consider analyzing your data in segments (e.g., before, during, and after a recession period) to see if promotions slowed down for everyone, which would confound an analysis that attributes faster/slower promotions solely to job switches.
Company Health: During financial uncertainty, companies may promote from within less frequently, or they might pivot strategy and promote employees more rapidly to fill management gaps after layoffs. That unpredictability means controlling for such variables is crucial in any robust statistical analysis.
Should an Intra-Company Promotion from “Data Scientist” to “Senior Data Scientist” Be Counted as a Job Switch?
Deciding whether an internal promotion counts as a “switch” is nuanced:
Definition of Switch: If your main hypothesis is about external job mobility, you might only count moves from one employer to another. However, if you believe role changes within a company also reflect mobility, you should include them as part of the switches.
Impact on Results: Counting internal role changes could inflate the total number of “switches” for stable employees who get promoted multiple times at the same company, thus possibly skewing the correlation between job switches and time to manager.
Data Consistency: You might find that the user_experiences table does not distinguish well between an internal promotion and an external move. Ensuring the dataset has a reliable company identifier is essential to differentiate these transitions.
How Do We Ensure the Records Reflect Actual Employment Durations When Employees Might Not Update Their Profiles on Time?
In user-generated or self-reported data, start_date or end_date fields might be inaccurate or delayed:
Validation: Cross-reference user-provided data with another source (e.g., HR records or payroll data) to validate or correct timeline inaccuracies.
Censoring: If a user fails to update the end date of a position, you might need to treat that job record as ongoing until a new position’s start date appears. This approach can introduce potential mismatches if there’s a gap in data entry.
Sensitivity Analysis: Conduct your analysis with both a strict filtering of “complete records only” and an inclusive approach that uses partial or approximate records. Compare results to see how sensitive your conclusion is to missing or delayed data updates.
How Do We Address the Issue That “Manager” Titles Might Vary in Scope and Responsibility?
Titles like “Data Science Manager” might mean different things depending on the company’s size, culture, or naming conventions:
Title Normalization: You might need to maintain a mapping table that normalizes different manager-like roles to a canonical “data science manager” title. For instance, one company might call it “Lead Data Scientist,” another might say “Director of Data Science.”
Managerial Responsibilities: Not all manager titles involve direct reports. Some roles labeled “manager” might be more akin to a senior individual contributor. Clarifying whether they truly manage people could be crucial for measuring a real “promotion.”
Edge Cases: If the title is “Data Science Manager” but the user’s actual responsibility is leading projects rather than people, your analysis might overcount. This can be mitigated if your dataset includes a field describing the role or number of direct reports.
How Would You Handle Scaling This Query for a Very Large Dataset (Millions of Rows)?
Performance and efficiency become critical with massive datasets:
Indexing: Ensure you have indexes on user_id, job_title, and start_date for faster lookups and sorting operations.
Partitioning: If your data spans multiple years or is extremely large, consider partitioning your table by user_id ranges, time ranges, or a combination of both. This reduces the data scanned for each query.
Distributed Computing: For extremely large datasets, you may need a distributed SQL engine like Apache Spark or a data warehouse solution like Snowflake or BigQuery. This ensures you can handle the joins and window functions without hitting memory or time limits on a single machine.
How Do You Account for Users Who Never Attain a Manager Role But Stay in the Dataset?
In many real-world cases, a significant portion of data scientists might never become managers:
Censored Data: From a time-to-event perspective, these are “right-censored”—they left the dataset or the observation period ended before they became a manager.
Exclusion vs. Inclusion: You could choose to exclude them if your sole interest is in the time to manager role. Alternatively, you might include them as data points that have not yet reached the event, which is especially important for survival analysis.
Potential Bias: Excluding such users might bias your dataset toward those who do eventually become managers, giving a distorted view of how frequently or quickly promotions happen. If the group who never becomes managers is large or systematic (e.g., they prefer individual contributor paths), your results could be skewed if you ignore them entirely.
How Would We Interpret Situations Where Someone Moves Repeatedly Between Data Scientist and a Different Field, Then Back to Data Scientist?
Some individuals might explore roles outside data science (e.g., Product Manager or Software Engineer) before returning to a data scientist path:
Defining a Continuous DS Career: If you want a pure data scientist track, you might disregard interruptions by non-data-science roles. Alternatively, you could interpret them as negative signals for a straightforward promotion path since they may delay their manager timeline.
Context-Specific Filters: Some companies value cross-disciplinary experience. If that’s the case, stepping out of a data science role might accelerate a future promotion. The data alone may not be enough to confirm the user’s motivations, but you can flag these intervals for additional analysis.
Tracking Gaps: Multi-year gaps outside data science might reset the “time to manager” logic if you assume continuous DS practice is essential for moving up. This depends heavily on how you want to define a consistent career track.
How Do Organizational Restructurings or Mergers Affect the Data?
Company reorganizations can rename roles, merge teams, or apply new title structures:
Renaming Impacts: A user could wake up one day and find “Data Scientist” turned into “Analytics Specialist” if the company decided on a rebranding. Without a real change in responsibilities or a promotion, your logic might interpret this as a switch.
Merger Duplicates: If two companies merge, employees might show up in the system with multiple overlapping job records. This could artificially inflate job switch counts unless you carefully merge those records.
Defensive Data Design: Keeping historical records of title changes at the organizational level helps. You can map old titles to new ones whenever a corporate event (like reorg) occurs rather than marking every employee as switched.