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Open nowFirst seen 14 hours ago

Quantitative Developer - Python/Django (LATAM, Remote)

iDelsoft - Get Hired. Work From Anywhere.34 open roles

Where
Remote
Work mode
Remote
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Your applicationOpen nowQuantitative Developer - Python/Django (LATAM, Remote)iDelsoft - Get Hired. Work From Anywhere. · Remote

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The clock on this job

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11.4% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
  1. 2.0%1 day
  2. 4.2%3 days
  3. 11.4%7 days
  4. 17.5%14 days
  5. 34.2%30 days
  6. 43.5%60 days
This job: first seen 14 hours ago

The posting

Quantitative Developer — Job Description

Role Definition

Reports To: Financial Engineering Manager Seniority: Senior Location: Open to candidates in LatAm, working EST hours

Owns scoring integrity end to end — implementation, validation harness, and production debugging — work currently handled ad hoc by the Principal Engineer.

What You'll Do

  • Build and maintain the production Python that computes PRISM and related risk scores — turning methodology into code that runs correctly and at scale
  • Build and maintain the reference-set harness that validates every model or classification change in CI
  • Diagnose scoring failures in production — distinguish code, data, and methodology issues — and fix the underlying class of bug, not just the instance
  • Rule on straightforward classification questions; escalate genuinely hard calls (structured products, buffered ETFs, private assets)
  • Estimate blast radius and maintain a tested rollback for every model or classification change before it ships
  • Keep the scoring path performant as portfolio and security volume grows

Skills & Requirements

Technical

  • Production Python you've shipped and maintained — not a prototype or notebook
  • Django — models, migrations, tests, CI, code review, to the same standard as any other engineering seat
  • SQL and data work at scale — pandas, numpy, portfolio-sized datasets
  • Testing & validation engineering — reference-set/golden-data harnesses wired into CI, not just unit tests
  • Large-scale systems, data pipelines, or automated systems (trading systems, scrapers, data adapters)

Domain

  • US market structure and asset classification — equities, fixed income, funds, ETFs, annuities, structured products, cash equivalents, private assets
  • Risk modeling and scoring fundamentals — volatility, correlation, concentration, tail measures
  • Hands-on options, structured products, or derivatives experience is a plus
  • Tax-aware analytics (after-tax return, cost basis, loss harvesting) is a plus — this would be built here, not maintained

Important Notes

  • Not a fit for someone whose experience is primarily research-grade quantitative code, notebooks, or prototypes — we need production software taken from development through deployment and maintenance
  • Looking for consistent employment history — 18+ month tenures in previous roles, demonstrating stability and long-term ownership
  • Work at the intersection of software engineering, quantitative finance, and fintech
  • Long-term opportunity to contribute to production systems used in real financial workflows

First 90 Days

  • Weeks 1–2 — take one live PRISM defect end to end and establish whether the cause is code, data or methodology
  • Weeks 3–6 — build the reference-set harness and wire it into CI as non-blocking
  • Weeks 7–12 — make it a required check, and take scoring incidents off the Principal Engineer

Interview Process

  1. Async Loom Screen — first-round async screen.
  2. Screening Interview — short live call: basic fit, motivation, communication, and logistics.
  3. Who Interview — chronological career walkthrough: for each role, what you were hired to do, what you're proudest of, the low points, who you worked with and what they'd say, and why you left.
  4. Focused / Technical Interview — deep-dive on the competencies for this seat, built around two role-specific exercises.
  5. Reference Interviews — calls with former managers and colleagues to verify track record, technical ability, and working style.
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