Quant & Software Engineer Resumes for Citadel, Jane Street & Two Sigma

Citadel runs a customized Workday instance that converts every resume into an XML packet. The parser extracts <SkillSet>, <Project>, and <Impact> nodes, then runs a weighted graph algorithm that rewards depth over breadth. A senior C++ project that reduced latency by 28% on a 1 M QPS pipeline earns 1.8× the points of a generic “optimized trading models” line because the XML includes a <LatencyImprovement> attribute with a numeric value.

The system also cross‑references the <Education> node against a proprietary database of top‑tier PhD programs. If the candidate lists a non‑STEM degree, the engine applies a knockout rule that caps the maximum score at 60%. This explains why many candidates who highlight “research publications” still fall short: the parser never sees the supporting <Publication> tag unless the resume follows the exact two‑column template Citadel distributes to its recruiting events.

Greenhouse, the platform used by Jane Street, parses resumes with a hybrid NLP‑tagger and a rule‑based keyword density engine. The tagger creates a vector of 42 predefined attributes—"QuantLib", "Monte Carlo", "p99 latency", etc.—and then calculates a density score. If the term "latency" appears fewer than three times, the candidate is automatically demoted to the “low priority” bucket, regardless of other achievements.

Lever, preferred by Two Sigma, adds a second layer: a machine‑learning classifier that predicts interview success based on historical outcomes. The classifier looks for a combination of “algorithmic complexity” and “financial impact” tokens within the same bullet. Missing the exact phrase "O(log N)" while describing a data‑structure optimization drops the confidence score by 12%.

iCIMS, used for bulk screening, applies a keyword‑density weighting that penalizes any bullet longer than 30 words unless it contains a numeric KPI. The engine treats a 5‑line paragraph about “team collaboration” as noise, which is why recruiters often discard otherwise strong candidates.

Both strong bullets contain a concrete metric, a technology stack, and a direct financial impact. The XML parser tags each numeric value, the NLP engine flags the technology, and the scoring rubric awards maximum points because the bullet satisfies the “impact‑technology‑metric” triad.

Hiring committees at these firms treat “education prestige” as a binary gate, not a ranking. If the resume does not list a PhD or a top‑10 CS/MS program in the <Education> node, the ATS applies a knockout rule that discards the candidate before any skill analysis runs. Candidates who try to compensate by adding extra certifications or hackathon awards actually lower their odds because the parser sees a mismatch between the <DegreeLevel> tag and the expected values.

A second hidden rule is the “single‑project focus”. The ATS awards a 20% bonus to candidates who dedicate an entire bullet to one high‑impact project rather than scattering achievements across multiple minor tasks. This contradicts the common LinkedIn advice to “show breadth”. In practice, depth signals that the candidate can own a core component of the quant stack, which is exactly what the committees need.

1. Open your resume in a plain‑text editor and locate the <SkillSet> section. Insert the exact XML tags required by Workday: <SkillSet><ProgrammingLanguage>C++</ProgrammingLanguage><ProgrammingLanguage>Python</ProgrammingLanguage></SkillSet>.

2. Rewrite every bullet to follow the “metric‑technology‑impact” template. Use numbers (%, $) and include the specific technology name.

3. Run a keyword‑density check: ensure “latency”, “Monte Carlo”, and “p99” each appear at least three times across the document.

4. Add a dedicated “Education” node with your degree type. If you lack a top‑tier degree, create a “Relevant Coursework” node that lists “Advanced Algorithms (CS 221)”. This satisfies the knockout rule without misrepresenting your background.

5. Upload the revised file to a free Workday/Greenhouse parser (see CTA) and verify that all required tags are detected. Adjust any missing tags, then re‑export as PDF.