First-job example · frozen engine output

A course project. A short internship. What can this CV show?

This fictional final-year student cleaned survey data in Python and helped with weekly reporting. Here is the engine's recorded assessment against one junior data analyst role.

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The student, university, internship team and vacancy are fictional. The engine output is from a real production free-scan request. Explanatory notes are ours.

This case extends the homepage's fictional survey-cleaning project. The output below comes from a separate recorded scan.

One English student run is shown unchanged on all three language pages. We have not run Chinese versions of this student case.

Captured 2026-09-13T11:14:58Z · model deepseek-v4-flash · reference FL-20260913-92a02185. This is one recorded result, not a live scan.

Another run may use different wording or reach a different assessment.

The model name is the configured request model for that deployment; the response itself did not attest it.

This run's structured coverage table was unavailable, so the complete engine text and score are shown instead.

01

Student case: preparing for a first analyst role

  • Target: junior data analyst in Singapore
  • Course project: Python data cleaning; a teammate made the charts.
  • Internship: updated an Excel tracker under a supervisor's review.
Read the exact CV and job description sent

Synthetic CV input · English

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.

Synthetic job description input · English

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

Captured result

Fit score

78/100

Verdict

Apply

03

Strongest angle

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

The engine's example rewrite

Before

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

After

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

Missing evidence

  • 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

Risky claims

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

Next step

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

Summary

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

Score breakdown

Score breakdown
CriterionScoreMaximumExplanation
Hard requirements coverage 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.
Experience relevanceBiggest gap 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.
Evidence quality 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.
Claim defensibility 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.

Scores and explanations come from this capture. Factor labels are interface text.

10

Stress test: the impossible trader claim

We also keep this older synthetic test. It planted a 2008 desk-leadership claim that conflicted with the CV's university timeline, to test whether the scan would challenge it.

Historical capture from September 2026. This preserves what that run returned; it is not a claim that today's engine will return identical results.

The trader case has separate English, Simplified Chinese and Traditional Chinese runs. Their results differ, as disclosed below.

Independent same-language re-runs were stable: English 42/42 and Simplified Chinese 28/28. This capture shows real cross-language divergence: English returned 42, “Needs more evidence”; both Chinese runs returned 28, “Skip”.

Captured 2026-09-04T06:13:43Z on deepseek-v4-flash, order FL-20260904-aa0c9666. Frozen output: run the same CV again and the wording will differ.

Read the archived trader stress test
11

Captured result

Fit score

42/100

Verdict

Needs more evidence

12

Strongest angle

Daniel's six years as an FX trader at StraitsBridge Bank, pricing G10 and Asia FX spot and forwards while partnering with quants on backtesting carry and momentum screens, is the most defensible match to the JD's requirement for FX product understanding and trader collaboration.

13

Fact-Checker refused the flagged claim

The lines that need work here are the ones flagged above; they need removing or evidence, not rewording.

The 2008 London desk claim must be cut or fully documented, and the "partnered with quants" and "led desk research sessions" bullets need artifacts before they can be sharpened. The graduate analyst bullet from 2013-2015 is the only weak-but-honest line that can be safely rewritten.

Before

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

After

"Built Python and VBA scripts for daily P&L explain, yield-curve checks, and FX forward-point sanity checks, supporting the desk's market data analysis workflow. [NEEDS PROOF: specific script or notebook that shows the Python implementation and the data sources used]"

[NEEDS PROOF] means evidence is still required. Do not treat marked wording as ready to submit.

14

Missing evidence

  • Any specific Python or SQL project: a data pipeline, a backtest framework, a signal analysis notebook, or a database query set with measurable outcomes.

  • Evidence of owning a model lifecycle: a named signal or strategy Daniel proposed, tested, refined, and handed to a desk with documented feedback.

  • A written research sample: a memo, a market note, or a presentation that demonstrates the "clear written communication in English" the JD requires.

  • Any quant research role title or formal research responsibility; "partnered with quants" is not the same as conducting research.

  • A plausible explanation for the 2008 London desk leadership claim, including dates, employment, and reporting structure.

15

Risky claims

3 lines·2 need proof·1 cut

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

#01

HIGH SEVERITY chronology conflict: 2008-2011 BSc Economics at University of Bristol versus "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."

Cut
  • Why an experienced reviewer would push back: The claim requires Daniel to have led a London FX desk during the 2008 financial crisis while the CV shows him as a full-time undergraduate in Bristol from 2008 to 2011. There is no part-time, placement, or internship explanation anywhere in the CV, and no employer is named for this role. The USD 50 billion book and USD 300 million P&L figures are extraordinary for any trader, let alone a student, and no measurement window or audit trail is provided.
  • One concrete artifact the candidate should bring to defend it: A signed employment contract or desk mandate letter from the named employer covering 2008, plus a P&L statement or broker statement for Q1 2008, and a letter from the university confirming the degree was completed part-time or with a placement year.
#02

"Senior trader, StraitsBridge Bank, 2021-2026. Led desk research sessions, reviewed model assumptions with technology, and mentored two analysts."

Needs proof
  • Why an experienced reviewer would push back: "Led desk research sessions" and "reviewed model assumptions" are vague seniority claims that do not establish research ownership. The JD asks for evidence of owning a model lifecycle from hypothesis to desk feedback; this bullet describes participation, not ownership. The 2021-2026 span also extends to the present with no end date, which is fine, but the promotion from trader to senior trader at the same bank is not explained with any performance rationale.
  • One concrete artifact the candidate should bring to defend it: A research session agenda or meeting notes where Daniel proposed a hypothesis, showed test results, and documented trader feedback, plus a promotion letter or performance review that names the specific achievements justifying the senior title.
#03

"FX trader, StraitsBridge Bank, 2015-2021. Priced G10 and Asia FX spot and forwards, maintained risk explain notes, and partnered with quants on backtesting carry and momentum screens."

Needs proof
  • Why an experienced reviewer would push back: "Partnered with quants on backtesting" is a supporting role, not a research role. The JD requires the candidate to test signals and review performance decay; this bullet suggests Daniel was a user of quant output, not a producer. The risk explain notes are a good communication artifact but are not described as research publications.
  • One concrete artifact the candidate should bring to defend it: A copy of one risk explain note that shows Daniel explaining a P&L move with a clear hypothesis and data, plus an email thread or document showing his specific contribution to a carry or momentum backtest.
16

Next step

Do not apply yet; gather a written research sample, a specific Python or SQL project artifact, and a documented example of a model or signal you personally owned from hypothesis to desk feedback, and remove or fully evidence the 2008 London desk claim before submitting.

17

Summary

The score reflects a genuine partial overlap: Daniel has real Python and SQL skills, direct FX and rates product knowledge, and years of trader-quant partnership experience that map to the JD's communication and collaboration requirements. The single biggest factor keeping the score down is the absence of any evidence that Daniel personally owned a model lifecycle from hypothesis to desk feedback; his CV describes partnering, reviewing, and mentoring, but never leading a research project end to end. Additionally, the CV contains a dated claim that cannot stand as written, which materially weakens overall credibility.

18

Score breakdown

Score breakdown
CriterionScoreMaximumExplanation
Hard requirements coverage 18 35 Strong Python and SQL are claimed but not demonstrated with specific projects; FX and rates product knowledge is solid and directly evidenced by six years of trading G10 and Asia FX.
Experience relevance 20 30 Trading experience is highly relevant to the JD's trader partnership requirement, but the CV shows no quant research role, no signal testing, and no model lifecycle ownership.
Evidence quality 3 20 The CV is almost entirely role descriptions without metrics, outcomes, or artifacts; the one concrete metric (USD 50 billion book, USD 300 million P&L) is attached to a chronology conflict.
Claim defensibilityBiggest gap 1 15 The 2008 London desk leadership claim directly conflicts with the 2008-2011 Bristol undergraduate degree and cannot be defended as written.

Scores and explanations come from this capture. Factor labels are interface text.

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