首次求職範例 · 已凍結的系統輸出

一個課程專題，一段實習：這份履歷能說明甚麼？

這位虛構的應屆學生用 Python 清理過問卷數據，也協助過每週報表工作。這裏展示系統針對一個初級數據分析職位給出的實際結果。

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學生、學校、實習團隊和職位均為虛構。系統輸出來自一次真實的線上免費掃描請求，說明文字由我們撰寫。

此案例延續首頁虛構的問卷數據清理專題，下方輸出來自另一次實際記錄的掃描。

三種語言頁面均原樣展示同一次英文學生案例結果。此學生案例尚未運行中文版本。

擷取時間：2026-09-13T11:14:58Z · 模型：deepseek-v4-flash · 編號：FL-20260913-92a02185。這是一次已記錄的結果，並非即時掃描。

再次運行時，措辭或判斷可能不同。

模型名稱來自該次部署的設定，回應本身未予確認。

此次運行的結構化覆蓋表不可用，因此改為展示完整的系統文字與分數。

01

學生案例：準備申請第一份分析工作

  • 目標：新加坡初級數據分析職位
  • 課程專題：負責 Python 數據清理，圖表由隊友製作。
  • 實習：在主管覆核下更新 Excel 跟進表。
查看實際傳送的完整履歷與職位描述

虛構履歷輸入 · 英文

Synthetic student S01
Location: Singapore
Target: Junior Data Analyst

Education
BSc Business Analytics, Synthetic University
September 2023 - expected May 2027
Final-year student. The degree is not yet completed.

Course project
Survey data cleaning, January - April 2026
- Cleaned survey responses in Python for a course project.
- Used pandas to remove duplicate rows, standardize response categories, and flag missing values.
- Recorded the cleaning steps in a notebook and explained the choices in a README.
- My contribution was data cleaning. A teammate produced the charts.
- This was coursework, not client work or a deployed analytics platform.

Internship
Operations intern, Synthetic Operations Team
June - August 2026
- Helped with weekly reporting.
- Updated an Excel tracker and checked entries for missing dates under a supervisor's review.
- Summarized unresolved entries for the supervisor.
- I did not own the reporting process or measure a business improvement.

Skills
Python and pandas used in the course project.
Excel sorting, filtering, and lookup formulas used during the internship.
SQL SELECT, WHERE, and GROUP BY practiced in coursework.
No production-database experience claimed.

All people and organizations in this CV are fictional.

虛構職位描述輸入 · 英文

Synthetic vacancy: Junior Data Analyst
Location: Singapore
This is a fictional job description, not a live vacancy.

We welcome final-year students and recent graduates for an entry-level analyst role.

Requirements
- Clean and check tabular data using Python or Excel.
- Write basic SQL queries, including joins.
- Explain data-cleaning choices and document the steps taken.
- Work with a supervisor and communicate issues clearly.
- Handle data within the team's access and confidentiality rules.

Useful additional experience
- Create a simple chart or dashboard and explain what it shows.

Applicants may use coursework or internship examples.
Production-system ownership and prior full-time analyst employment are not required.
02

已記錄的結果

配對分數

78/100

判斷

Apply

03

最有力的切入點

The candidate can defend a full data-cleaning workflow end to end: pandas dedupe, category standardization, missing-value flagging, a notebook of steps, and a README explaining the choices, which is precisely the JD's core cleaning-and-documentation requirement.

04

系統給出的範例改寫

原文

Used pandas to remove duplicate rows, standardize response categories, and flag missing values.

改寫後

Cleaned survey responses in Python with pandas: removed duplicate rows, standardized response categories, and flagged missing values, then recorded each step in a notebook and explained the choices in a README.

The rewrite keeps every action the source states and pulls the documentation step (already in the CV) into the same bullet so it answers the JD's "explain data-cleaning choices and document the steps" requirement in one line. It adds no outcome, no scale, and no tooling beyond what the source names. If the candidate can state the number of responses cleaned or the categories standardized, that would let the bullet say more, but only with the actual figure.

05

待補證據

  • No evidence of SQL joins, which the JD names explicitly alongside basic queries.

  • No chart or dashboard produced by the candidate; the CV states a teammate produced the charts.

  • No stated familiarity with the team's access or confidentiality rules, only supervised work.

  • No artifact named for the internship tracker (no sample, no schema, no size).

06

風險表述

This is the interview attack surface, what a careful reviewer is most likely to push back on, not a list of mistakes. Read it as preparation.

#01

"SQL SELECT, WHERE, and GROUP BY practiced in coursework."

  • Why an experienced reviewer would push back: the JD asks for joins, and the CV lists three clauses without joins, so a reviewer will test whether the candidate can actually write a join or has only seen one demonstrated.
  • One concrete artifact to bring: a short notebook or .sql file from coursework showing at least one join the candidate wrote, with the two tables and the join key visible.
#02

"Helped with weekly reporting" during the June to August 2026 internship.

  • Why an experienced reviewer would push back: "helped with" is vague on what the candidate personally did versus observed, and the JD wants clear communication of issues to a supervisor, which needs a concrete instance.
  • One concrete artifact to bring: a redacted copy of one weekly tracker update plus the summary of unresolved entries the candidate sent to the supervisor.
#03

"Updated an Excel tracker and checked entries for missing dates under a supervisor's review."

  • Why an experienced reviewer would push back: the scope is narrow (missing dates only) and the reviewer cannot tell the tracker's size, the error rate, or whether the candidate found anything, so the line reads as task-following rather than analysis.
  • One concrete artifact to bring: a redacted before/after view of the tracker showing the entries flagged and the supervisor's review notes.
07

下一步

Apply, and before submitting, write one small SQL exercise that uses a join on two tables and keep the file, so the SQL line can name joins honestly rather than only SELECT, WHERE, and GROUP BY.

08

結果摘要

The CV maps cleanly onto the core of this JD: the course project states the candidate personally cleaned survey responses in Python with pandas (dedupe, category standardization, missing-value flagging), recorded the steps in a notebook, and explained the choices in a README, which is exactly the "clean and check tabular data" plus "explain data-cleaning choices and document the steps" pair. The internship adds supervised Excel tracker work and summarizing unresolved entries to a supervisor, which matches the "work with a supervisor and communicate issues" requirement. The single biggest factor keeping the score from being higher is SQL: the CV only claims SELECT, WHERE, and GROUP BY practiced in coursework, and the JD explicitly asks for joins, which are not named anywhere in the evidence.

09

分數明細

分數明細
評分項目得分滿分說明
硬性要求覆蓋 27 35 Python/pandas cleaning, documentation, supervisor communication, and confidentiality-adjacent supervised work are directly stated; SQL joins are the one named requirement with no evidence.
經驗相關度最大短板 22 30 The course project and the operations internship are both entry-level and directly in the data-cleaning and supervised-reporting lane the JD describes, though neither is analyst-titled.
證據質量 17 20 The CV is unusually specific about scope and limits (notebook, README, "my contribution was data cleaning", "I did not own the reporting process"), which is strong for a student.
陳述可辯護度 12 15 Almost every line is already hedged to what the candidate can defend; the only soft spot is the SQL skill line, which names clauses but not joins.

分數與解釋來自本次擷取，評分項目名稱屬於介面文字。

10

壓力測試：無法自洽的交易員陳述

我們亦保留這項較早的虛構測試：在履歷中植入一項與大學就讀時間衝突的「2008 年領導交易台」陳述，檢查掃描是否會提出質疑。

這是 2026 年 9 月的歷史結果，保留當時實際返回的內容，不表示目前系統會給出完全相同的結果。

交易員案例分別運行了英文、簡體中文和繁體中文版本。結果存在差異，詳見下方說明。

同一語言的獨立重跑結果穩定：英語為 42/42，簡體中文為 28/28。這次捕獲也如實顯示了跨語言差異：英語返回 42「Needs more evidence」，兩次中文運行均返回 28「Skip」。

2026-09-04T17:02:57Z 用 deepseek-v4-flash 跑出，order FL-20260904-637c4fe8。這是凍結的輸出：同一份 CV 再跑一次，措辭會不一樣。這次結果在新加坡時間 2026-09-05、語言修正 399001f 後捕獲。

查看保留的交易員壓力測試
11

已記錄的結果

配對分數

28/100

判斷

Skip

12

最有力的切入點

FX 交易實務經驗（2015-2026 於 StraitsBridge Bank 定價 G10 與亞洲 FX 即期及遠期）能提供市場直覺，這是量化研究與交易團隊溝通的基礎，但僅此一項不足以彌補研究能力的缺口。

13

Fact-Checker 拒絕改寫被標記的表述

若無法提供，此陳述應從 CV 中移除。

這次運行沒有改寫被標記的 2008 年經歷；它把改寫示例放在另一條未被標記的 bullet 上。

原文

Built VBA and Python scripts for daily P&L explain, yield-curve checks, and FX forward-point sanity checks.

改寫後

Developed Python and VBA scripts to automate daily P&L explain, yield-curve validation, and FX forward-point sanity checks for the trading desk. [NEEDS PROOF: 腳本是否包含統計檢定或異常偵測邏輯，而非僅資料整理]

此改寫僅強化動詞與 JD 詞彙對齊（Python、FX 產品），未添加任何來源未支持的事實。若候選人能提供腳本實際使用的分析邏輯或輸出範例，可進一步補充；否則維持現狀為誠實表述。

[NEEDS PROOF] 表示仍需補充證據，請勿將帶此標記的文字直接提交。

14

待補證據

  • 任何獨立完成的量化研究專案，包含假設、資料處理、回測與結果分析。

  • Python/SQL 用於市場數據分析的具體範例（如程式碼庫、分析報告或可展示的 notebook）。

  • 模型生命週期主導經驗：從訊號發想到 trader 回饋的完整過程記錄。

  • 可發布的實務研究樣本（如內部備忘錄、研究筆記或交易策略文件）。

  • FX 或 rates 產品的量化定價或風險模型開發證明，而非僅交易執行經驗。

15

風險表述

3 lines·3 need proof·0 cut

此為面試攻擊面，即審查者最可能質疑之處，非錯誤清單；候選人應視為準備方向。

#01

LED THE ENTIRE LONDON FX TRADING DESK DURING THE 2008 FINANCIAL CRISIS, MANAGING A USD 50 BILLION BOOK AND DELIVERING USD 300 MILLION P&L IN THE FIRST QUARTER.

NEEDS PROOF
  • Why an experienced reviewer would push back: CV 顯示 2008-2011 為 University of Bristol 全職學士學生，且 2013 年才開始第一個就業職位（Graduate analyst）；此陳述要求候選人在 2008 年同時身處倫敦管理大型交易檯，與教育時間軸直接衝突，且無任何兼職、實習或休學解釋。此外，全大寫格式與缺乏機構名稱、職稱或任何佐證，使此聲明無法在面試中防禦。
  • One concrete artifact the candidate should bring to defend it: 若此經歷屬實，需提供 2008 年倫敦僱主的正式聘書、職稱證明及經審計的 P&L 報表；若無法提供，此陳述應從 CV 中移除。
#02

Senior trader, StraitsBridge Bank, 2021-2026. Led desk research sessions, reviewed model assumptions with technology.

NEEDS PROOF
  • Why an experienced reviewer would push back: 「Led desk research sessions」與「reviewed model assumptions」暗示研究主導角色，但 CV 其他部分僅描述交易與支援 quants 工作；缺乏具體研究輸出、方法論或成果，無法證明此為量化研究而非交易團隊例行會議。
  • One concrete artifact the candidate should bring to defend it: 一份由候選人主導的研究會議議程、簡報或內部研究備忘錄，顯示具體的模型假設審查內容與結論。
#03

MSc Computational Finance, City University of Hong Kong, 2012-2013.

NEEDS PROOF
  • Why an experienced reviewer would push back: 此學位提供量化金融訓練，但 CV 未展示任何課程專案或論文主題與 FX/rates 建模相關；僅列學位名稱不足以證明具備 JD 要求的 Python 建模能力。
  • One concrete artifact the candidate should bring to defend it: 課程中與 FX 或 rates 相關的期末專案報告、程式碼或論文摘要，證明 computational finance 訓練已轉化為實際建模技能。
16

下一步

此 CV 目前不符合量化研究職位要求，建議先移除 2008 年倫敦交易檯經歷（或補齊完整證明），並累積至少一個從假設到 trader 回饋的獨立研究專案後，再考慮申請此類職位；短期可先申請交易支援或研究助理角色以建立可防禦的量化研究履歷。

17

結果摘要

本 CV 的量化研究經驗屬輔助性質，缺乏 JD 要求的模型生命週期主導證據；且 2008 年倫敦交易檯經歷與 CV 所列教育及就業時間軸直接衝突，無法以現有證據辯護。分數反映核心要求（Python/SQL 建模、FX/rates 產品深度、可執行研究發表）均未有直接且可防禦的證明。

18

分數明細

分數明細
評分項目得分滿分說明
硬性要求覆蓋最大短板 8 35 Python 與 SQL 僅列為技能，無獨立研究或建模專案證明；FX/rates 產品理解來自交易經驗，但未展示量化分析深度。
經驗相關度 10 30 交易背景與 FX 產品相關，但「與 quants 合作回測」屬支援角色，非主導研究；無任何模型從假設到 desk feedback 的擁有權證據。
證據質量 6 20 多數經歷描述籠統（如「led desk research sessions」），缺乏可驗證的量化成果或具體研究輸出。
陳述可辯護度 4 15 2008 年倫敦交易檯經歷與 CV 教育及就業時間軸直接衝突，且全大寫陳述缺乏任何佐證，屬高風險表述。

分數與解釋來自本次擷取，評分項目名稱屬於介面文字。

從你真正做過的事開始。

準備一份履歷和一份職位描述，檢查經驗能支持哪些表述，以及還欠哪些證據。

免費掃描我的履歷毋須帳戶。