TikTok·Data Scientist·Take-home Assignment·Intermediate

Intermediate

May 2026

Summary

TikTok data scientist interview with a practical Pandas/ecommerce scenario. The whole thing felt more like a take-home exercise than a live coding session, which I wasn't expecting.

Questions Asked (3)

Given a raw ecommerce sales table, use Pandas to filter for European orders placed in the last 30 days and compute their total revenue.

Product Analytics & MetricsAlgorithms & Data Structures

Author's notes

The filtering part was fine.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the schema and assumptions (date column, country field, revenue column). Then walk through a Pandas pipeline: parse dates, filter by European countries and last 30 days, and sum revenue. Finally, mention edge cases like time zones, missing data, and performance considerations.

Pro tip: Always confirm the definition of 'European orders' (e.g., shipping country vs. billing country) and whether revenue should be net of returns/discounts. Also, use vectorized operations and avoid loops for scalability.

1. Clarify data schema and assumptions

Ask about column names, date format, country field, and revenue definition. Confirm what 'European' means and whether 'last 30 days' is relative to today or max date in data.

2. Load and preprocess data

Read the table into a Pandas DataFrame. Convert the date column to datetime and ensure revenue is numeric. Handle missing values if necessary.

3. Filter for European orders in last 30 days

Create a boolean mask for European countries (e.g., using a list or ISO codes) and another for dates within the last 30 days. Combine masks to filter the DataFrame.

4. Compute total revenue

Sum the revenue column of the filtered DataFrame to get the total. Optionally, group by country or date for further insights.

5. Validate and discuss edge cases

Check for anomalies, time zone issues, and data completeness. Mention how you would handle large datasets (e.g., using Dask or chunking).

Key Points to Mention

Use pd.to_datetime with errors='coerce' to handle invalid dates.
Define European countries via a list or mapping (e.g., ISO 3166 alpha-2 codes).
Use vectorized operations like df.loc for filtering instead of loops.
Consider time zone awareness: convert dates to UTC or a consistent timezone.
Handle missing or negative revenue values appropriately (e.g., exclude returns).
For large data, use efficient dtypes (e.g., category for country) or chunking.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.

Identify and remove duplicate orders from the dataset, then describe how you would handle missing revenue values.

Data ModelingTechnical Trade-offs

Author's notes

Straightforward dedup with duplicated() and drop_duplicates().

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AI HintsAI Generated

Suggested Approach

Start by clarifying the definition of a duplicate order and the business context, then outline a systematic deduplication process using SQL or pandas. For missing revenue, discuss the trade-offs between deletion, imputation, and model-based approaches, and recommend a strategy based on the analysis goal.

Pro tip: Always validate your deduplication logic with a small sample and check for unintended data loss. For missing revenue, consider creating a binary flag for missingness and analyze patterns before deciding on imputation.

1. Clarify Definitions and Context

Ask clarifying questions to define what constitutes a duplicate order (e.g., same order ID, same customer and timestamp) and understand the business impact of duplicates and missing revenue.

2. Identify Duplicates

Use grouping and aggregation to identify duplicate records based on key columns, and quantify the extent of duplication.

3. Remove Duplicates

Choose a deduplication strategy (e.g., keep first/last occurrence, aggregate values) and implement it using tools like SQL window functions or pandas drop_duplicates.

4. Handle Missing Revenue

Analyze the pattern of missingness, then decide on an approach: deletion, mean/median imputation, model-based imputation, or flagging as a separate category, considering the analysis goal.

5. Validate and Document

Validate the cleaned dataset by checking summary statistics and ensuring no unintended data loss, and document the steps and assumptions for reproducibility.

Key Points to Mention

Definition of duplicate orders: based on order ID, customer ID, timestamp, or other business keys.
Deduplication techniques: SQL ROW_NUMBER() with PARTITION BY, pandas drop_duplicates with subset and keep parameters.
Missing data mechanisms: MCAR, MAR, MNAR and their implications for handling missing revenue.
Imputation methods: mean/median, regression, multiple imputation, and their trade-offs.
Impact of missing revenue on downstream analysis: bias, reduced statistical power.
Best practices: create a missing indicator, validate with holdout set, document assumptions.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.

Generate summary statistics for the sales data and produce two visualizations that highlight trends across product categories.

Product Analytics & MetricsRoot Cause Analysis

Author's notes

Did a groupby on product_category with sum and mean for revenue, then a bar chart and a line chart over time.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the dataset structure and business context, then compute summary statistics (e.g., total sales, average order value, growth rates) segmented by product category. For visualizations, choose a time-series line chart to show trends and a bar chart or heatmap to compare categories, ensuring they highlight actionable insights for TikTok's product analytics.

Pro tip: Always tie your summary statistics and visualizations back to business metrics like engagement or revenue impact, and mention how you would handle missing data or outliers—this shows you think beyond the code.

1. Clarify Data and Objectives

Ask about the dataset (columns, time range, granularity) and the goal (e.g., identify top-performing categories, detect seasonality). Confirm the definition of 'sales' and 'product categories'.

2. Compute Summary Statistics

Calculate overall and per-category metrics: total sales, mean/median, standard deviation, growth rates, and percentage contribution. Use pandas describe() and groupby() for efficiency.

3. Design Visualizations

Create a time-series line chart (e.g., monthly sales per category) to show trends, and a bar chart or heatmap to compare categories. Ensure labels, legends, and colors are clear and accessible.

4. Interpret and Communicate Insights

Highlight key findings: which categories are growing/declining, outliers, and potential reasons. Relate insights to business actions (e.g., inventory, marketing).

5. Validate and Iterate

Check for data quality issues (missing values, outliers) and consider alternative visualizations if needed. Mention how you would validate findings with stakeholders.

Key Points to Mention

Data cleaning and preprocessing steps (handling missing values, outliers, date parsing)
Choice of summary statistics (mean vs. median, growth rates, seasonality)
Visualization best practices (appropriate chart types, clarity, interactivity if using tools like Tableau or Plotly)
Segmentation by product category and time period (e.g., weekly, monthly)
Business relevance: how insights could inform TikTok's product strategy or marketing
Scalability: how to handle large datasets (e.g., using SQL, Spark, or sampling)

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.