Junior Quantitative Developer

Summary

The Multi-Asset Solutions Team (MAST) manages a suite of active investment ETFs and delivers standard and customized multi-asset portfolio solutions across approximately $5 billion in AUM. Portfolios span U.S. and international equities, fixed income, and commodities. MAST’s investment process combines systematic quantitative models with a qualitative investment overlay. The Quantitative Research function is central to this process, designing, maintaining, and enhancing the models that translate market and macroeconomic information into portfolio allocations. These include:

• Market regime and business-cycle detection models
• State-space and signal-aggregation frameworks
• Bespoke portfolio optimization engines
• Scenario analysis and Monte Carlo simulations for outcome evaluation
  
  

The Junior Quantitative Developer plays a central role in building and maintaining the production infrastructure that powers MAST’s systematic investment process. This is a hands-on engineering role focused on productionizing quantitative models, operating reliable portfolio construction pipelines, and building the tools and systems that translate research into tradeable portfolios. The role partners closely with researchers, portfolio managers, and IT to ensure that MAST’s models run robustly, repeatedly, and at scale.

Key Responsibilities

• Own the architecture, operation, and maintenance of MAST’s systematic portfolio production platform.
• Build and maintain scalable, reliable pipelines for portfolio construction, data processing, and model execution.
• Deliver optimized and implementation-ready portfolios for PM review with a focus on robustness and repeatability.
• Design and implement tools to translate model outputs into tradeable portfolios, including override and constraint frameworks.
• Partner with research, PMs, and IT to productionize models and improve system performance.
  
  

This role sits at the intersection of software engineering, quantitative finance, and production operations. It requires strong Python skills, a systems-building mindset, and genuine interest in financial markets and quantitative methods.

Portfolio Production & Implementation

• Own and operate the end-to-end systematic portfolio construction pipeline, ensuring reliability, scalability, and auditability.
• Design, build, and maintain production systems for data ingestion, transformation, model execution, and portfolio generation.
• Implement automation, monitoring, logging, and alerting to ensure production stability and rapid issue detection.
• Develop validation frameworks to ensure data integrity and correctness of portfolio outputs. Troubleshoot production issues across data, models, and infrastructure.
  
  

Research Deployment & Quant Development

• Productionize quantitative models, signals, and portfolio construction methodologies developed by the research team.
• Build reusable libraries and tools for optimization, risk modeling, and constraint handling.
• Support back-testing and research workflows by developing scalable and consistent infrastructure.
• Collaborate with researchers to ensure alignment between research code and production systems.
  
  

Key Behavioral Expectations

Drives for Results

• Takes ownership of production reliability and treats system failures as personal accountability.
• Delivers robust, well-tested code aligned with Harbor’s investment objectives and production standards.
  
  

Unleashes Innovation

• Proactively identifies opportunities to improve system performance, code quality, and workflow automation.
• Brings a builder’s mentality — eager to learn quantitative methods and financial markets while maintaining engineering discipline.
  
  

Communication & Engagement

• Communicates clearly about system status, production issues, and technical tradeoffs.
• Works effectively with researchers, PMs, and IT, bridging the gap between research prototypes and production-quality systems.
  
  

Minimum Qualifications

• Bachelor’s degree in a quantitative or technical discipline (e.g., computer science, software engineering, mathematics, statistics, physics, data science); advanced degree a plus but not required.
• Strong proficiency in Python required, with demonstrable experience writing clean, maintainable code. Experience with databases (SQL/PostgreSQL), version control (Git), and production engineering practices preferred.
• The ideal candidate is a strong software developer with genuine interest in systematic investing and quantitative methods. We value engineering talent with intellectual curiosity about markets — you will learn the investment side on the job.
• 0–3 years of professional experience in software development, quantitative development, or a related technical role. Strong new graduates with relevant project work or internship experience will be considered.
• Interest in financial markets and quantitative investing is a plus. No finance credentials are required — we are hiring for engineering aptitude and willingness to learn.
  
  

Knowledge, Skills, & Abilities Required

The ideal candidate is a careful, detail-oriented developer who writes code they’d be comfortable maintaining a year from now. They are eager to learn quantitative finance, comfortable asking questions, and motivated by the challenge of building systems where correctness matters.

• Strong Python programming skills with an emphasis on clean, testable, production-quality code.
• Foundational understanding of statistics, linear algebra, and optimization concepts. Exposure to machine learning or time-series analysis is a plus.
• Systems-building mindset — thinks about reliability, edge cases, logging, and maintainability, not just getting code to run.
• Comfortable working in a small, fast-paced team where you will learn on the job and take on real responsibility quickly.
  
  

Preferred Skills

• Experience with relational databases (PostgreSQL, SQL Server) and writing efficient queries.
• Familiarity with data pipeline design, ETL workflows, scheduling tools (e.g., Prefect, Airflow), and monitoring/alerting patterns.
• Experience with version control (Git), testing frameworks, and CI/CD practices.
• Exposure to financial data (Bloomberg, market data APIs), quantitative libraries (NumPy, pandas, SciPy, CVXPY), or portfolio analytics is a plus but not required.
  
  

Compensation Pay Range: This position offers a competitive base salary range of $125,000 – $185,000, commensurate with experience and qualifications.