Quantitative Developer - Python/Django (LATAM)
TypeFull-time job
LocationUnited States
Posted2 hours ago
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
Async Loom Screen — first-round async screen.
Screening Interview — short live call: basic fit, motivation, communication, and logistics.
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.
Focused / Technical Interview — deep-dive on the competencies for this seat, built around two role-specific exercises.
Reference Interviews — calls with former managers and colleagues to verify track record, technical ability, and working style.
Originally posted on Himalayas
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
Async Loom Screen — first-round async screen.
Screening Interview — short live call: basic fit, motivation, communication, and logistics.
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.
Focused / Technical Interview — deep-dive on the competencies for this seat, built around two role-specific exercises.
Reference Interviews — calls with former managers and colleagues to verify track record, technical ability, and working style.
Originally posted on Himalayas
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