Senior Analyst, Investment Risk and Data Science

Key Responsibilities

• Analyze portfolio performance, positioning, characteristics, and risk using qualitative and quantitative information from portfolio management teams, researchers, market data, analytical platforms, and internal databases.
• Develop and clearly articulate investment insights that help the Investment Research team and other decision-makers understand portfolio outcomes, exposures, and sources of risk.
• Collaborate with Analysts and Research Directors on recurring oversight of existing subadvisors and the underwriting of prospective subadvisors.
• Design, build, test, and maintain analytical tools, applications, and data workflows using Python, R, SQL, or other appropriate technologies.
• Improve the reliability and scalability of recurring analytical processes through automation, validation, monitoring, and appropriate controls.
• Establish and follow sound development practices, including version control, code review, testing, documentation, reproducible analysis, and clearly defined ownership.
• Review and improve existing code, models, and analytical workflows to reduce operational risk, technical debt, and reliance on individual team members.
• Create clear technical and process documentation so that analytical tools and workflows can be understood, maintained, and supported by multiple members of the team.
• Help develop shared coding standards, reusable components, and common analytical frameworks across the IRDS team.
• Support the technical development of other team members through collaboration, code review, knowledge sharing, and informal mentoring.
• Identify gaps or inefficiencies in the team’s methods for measuring and reporting strategy performance, characteristics, and risk, and take ownership of implementing improvements.
• Prepare accurate and time-sensitive analytical reports for Harbor’s Board of Directors, internal investment committees, and other senior stakeholders.
• Participate in due diligence meetings with current and prospective subadvisors and synthesize quantitative and qualitative findings into actionable investment perspectives.
• Provide analytical support for fund-specific materials, webinars, client communications, and other external content as business needs require.

Key Behavioral Expectations

• Drives for Results
• Courageous and Resilient
• Agility and Adaptability
• Operates with agency, independence, and proactive problem solving

Minimum Qualifications

• Bachelor’s degree in finance, economics, mathematics, statistics, computer science, engineering, or a related field.
• 5-10 years of relevant professional experience.
• Experience in, or strong knowledge of, the asset management industry.
• Strong proficiency in Python or R and SQL.
• Ability to independently investigate open-ended investment and data questions.
• Ability to learn new technologies, data sources, and analytical methods.
• Strong attention to detail and a demonstrated commitment to data quality and analytical accuracy.
• Ability to work effectively in a fast-paced and evolving environment.
• Strong collaboration and communication skills, including the ability to explain technical and investment concepts to audiences with varying levels of expertise.
• Strong sense of ownership, responsiveness, and follow-through.

Preferred Qualifications

• Demonstrated experience developing, maintaining, and improving code-based analytical tools or data workflows in a professional environment.
• Experience testing, debugging, reviewing, and documenting analytical code.
• Ability to design solutions that are reliable, reusable, maintainable, and understandable to other team members.
• Experience analyzing, or a strong interest in analyzing, portfolio risk across equity, fixed income, commodity, or multi-asset strategies.
• Experience designing reusable Python or R packages, libraries, modules, or shared analytical components.
• Experience implementing automated testing, data validation, workflow monitoring, or continuous integration processes.
• Experience working with investment data platforms such as FactSet and Morningstar Direct.
• Familiarity with software-development principles applicable to analytical environments, including modular design, separation of concerns, dependency management, and reproducibility.
• Demonstrated ability to use large language models and agentic AI tools to improve analytical development, documentation, testing, quality assurance, and workflow efficiency.
• Sound judgment regarding the controlled and responsible use of AI tools with proprietary data, investment information, and analytical processes.