Junior Quantitative Developer

Harbor Capital Advisors, Inc.•New York, NY

About The Position

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. 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. 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.

Requirements

Bachelor’s degree in aсquantitative 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.
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.
Strong Python programming skills with an emphasis on clean, testable, production-quality code.
Foundational understanding of statistics, linear algebra, and optimization concepts.
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.

Nice To Haves

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.
Exposure to machine learning or time-series analysis is a plus.
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.

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.
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.
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.