最近，許多學生表示有興趣申請 DoorDash interview. 我們的團隊最近幫助一位候選人完成了跨領域的 DoorDash 訪問取得了極佳的效果，今天我們就來分享一下這個過程。
背景資料
這位應徵者從統計轉型到編碼，在中國有三年的資料分析經驗。他們的履歷很紮實，也廣泛地練習了 SQL，但他們對矽谷新創公司快節奏的面試方式感到吃力。緊張的情緒讓他們錯過了關鍵細節，表達的想法也不清晰。幸運的是，他們在面試前一週預約了 ProgramHelp 的遠端面試協助服務，這讓他們獲得了錄取機會。
許多人以為面試只靠技術實力，但實際上，面試考的是現場反應能力、溝通技巧、冷靜度，以及最重要的--是否有人支持您。
面試體驗
我們的客戶是一位從統計到編碼的轉換者，擁有三年的資料分析經驗，目標是應對 DoorDash。雖然他們的履歷和 SQL 技能都很強，但卻不習慣初創公司高速的面試節奏。緊張的情緒讓他們忽略了細節，說話滔滔不絕。我們在面試過程中的即時指導幫助他們保持了清晰的思路，最終成功獲得了錄用。
Question 1
“How would you design an experiment to test the impact of a new driver incentive program on delivery times?”
This question assesses not just statistical knowledge but business acumen and experimental rigor. The candidate knew to use A/B testing but hesitatedwhen detailing group selection, key metrics, and result analysis. We provided a real-time framework on their secondary device:
- Start with the experiment goal: Clarify what problem the incentive program aims to solve (e.g., reducing delivery times).
- Detail the design:
- User segmentation: How to split drivers into control and experimental groups (e.g., random assignment, stratified by region).
- Core metrics: Average delivery time, driver acceptance rate, order volume.
- Statistical analysis: Significance testing (t-tests for delivery times, chi-squared for acceptance rates), sample size calculation based on historical variability anddesired effect size.
With this structure, the candidate spoke more fluently and confidently, impressing the interviewer. When asked about sample size determination, weprompted factors like historical data variance, expected improvement, and statistical power (e.g., 80% power, 5% significance level),ensuring a thorough response.
Question 2
Given a table with columns: order_id, driver_id, order_time, pickup_time, dropoff_time, city_id, write a SQL query to find the average delivery time for each city.
The candidate quickly wrote a basic GROUP BY city_id query, but the interviewer added complexity:
“Modify the query to include only orders from the last month and drivers who completed at least 100 deliveries in that period.”
They needed to handle time windows and driver activity filters. We suggested:
- Use
WHERE order_time >= DATE_SUB(CURRENT_DATE(), INTERVAL 1 MONTH)for the time constraint. - Calculate each driver’s delivery count with a subquery or window function (e.g.,
COUNT(*) OVER (PARTITION BY driver_id)), then filter usingHAVING COUNT(*) >= 100after grouping bydriver_id.
The candidate refined their query step-by-step, incorporating DATEDIFF(dropoff_time, pickup_time) for delivery time and nested subqueries for clarity. The interviewer then discussed edge cases (e.g., missing timestamps), where we guided them tosuggest data validation (IS NOT NULL) and imputation strategies.
這是「作弊」嗎？
我們不會捏造答案或為候選人說話。我們的角色是
- 恢復清晰度 當緊張混淆思考時。
- 提供結構框架 (例如，將實驗設計分解為目標 → 設計 → 分析）。
- 填補小缺口 (例如，SQL 語法提醒），確保您現有的知識連貫一致地發揮出來。
不要單打獨鬥
ProgramHelp 支援頂尖科技公司的應徵者，例如 Google、DoorDash、Amazon、Microsoft，幫助您：
- 在複雜的問題解決過程中保持正軌。
- 簡潔、有邏輯地溝通想法。
- 將技術能力轉化為面試信心。
如果您正在準備 Google、DoorDash、Amazon 或類似的科技面試，卻對現場表現或解決問題的流程缺乏信心，請聯絡我們。讓我們成為您的安全網！
準備好提升您的面試技巧了嗎？ 聯絡我們 今天。
