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# Quantitative Developer

- **Company:** Poesis LLC
- **Location:** Menlo Park, CA, United States
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** FactSet, Application Programming Interfaces (APIs), Airflow, Business Analytics Applications, Big Data, Software Quality, Database Schema, Python (Programming Language), Machine Learning, NumPy, Backtesting, SciPy, SQL Databases, Model Validation, Git, Pandas, Matplotlib, Information Technology, Data Pipelines, Docker
- **Published:** September 4, 2026
- **Apply:** https://startup.jobs/quantitative-developer-poesis-8144257

## About the Role

* 3+ years of professional experience building the model infrastructure, data pipelines, and analytical tools to drive trading strategies
 * Strong Python skills (pandas, numpy, scipy, matplotlib); comfort with SQL.
 * Skill working with Claude Code, Codex, or other coding agents.
 * Proficiency working with real-world financial datasets and building reproducible analyses or pipelines.
 * Understanding of statistics, regression, optimization, and ML fundamentals.
 * Clear communicator who can explain technical findings to non-specialists.
 * BS/MS/PhD in Computer Science, Mathematics, Statistics, Physics, Finance or related quantitative field.

Preferred Competencies

 * Prior full-time experience in finance, data science, or ML engineering.
 * Familiarity with APIs from Bloomberg, CapIQ, FactSet, or Refinitiv.
 * Exposure to portfolio optimization, risk modeling, or financial time-series.
 * Skill with git, Docker, and modern orchestration tools (Prefect, Airflow, etc.).
 * Early-stage startup experience or demonstrated builder mindset.

## Description

We're hiring a Quantitative Developer to help turn research ideas into production-grade code. You'll help build data pipelines, implement models and ensure results are clean, reproducible and explainable. You'll work alongside Poesis' Chief Scientist, CEO and engineering leadership to turn large-scale data and quantitative research into models, signals and tools that drive investment decision-making., * Rapidly implement and iterate on research ideas and model prototypes.

 * Clean, process, and join financial and fundamental datasets from professional and public sources.
 * Build and maintain processes for feature generation, back-testing, and model evaluation.
 * Run experiments, summarize results, and report findings to leadership.
 * Contribute to code quality: testing, documentation, and integration into shared systems.
 * Support the team in defining data schemas, APIs, and reproducibility standards.
 * Implement, test, and refine models, signals, and analytical workflows.
 * Maintain a consistent cadence of deliverables, focusing on iteration speed and reliability.


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