Portfolio Analytics Intern

Ideal Profile

Finance or quantitative background with genuine interest in data, analytics, and technology. Curiosity, organization, and eagerness to learn matter as much as raw technical skill. The ideal candidate has used AI-assisted development workflows in practice and can scope, prioritize, and build independently without a detailed playbook.

This role offers direct exposure across multiple focus areas — direct and secondary investment analysis, portfolio analysis and construction, and the development of tools and workflows that drive real operational value. It is a high-learning environment with broad cross-functional visibility, working alongside investment, technology, and operations teams from day one.

Key Responsibilities

• Monitoring & Reporting: Track all fund and direct investments, including capital inflows and outflows, underlying portfolio investments, and fund/company-level performance metrics; upload data to internal systems; support the team on ad-hoc requests.
• Data Management: Manage collection processes and outputs, maintain organized databases, and work on data architecture and systems planning.
• Infrastructure Planning & AI Initiatives: Develop data structures to support other teams' data needs across fund and direct investments. Build and ship AI-powered tools, automations, and data applications to streamline extraction, analysis, and presentation. Contribute to firmwide infrastructure including Notion database architecture and cross-functional process documentation.
• Analytics & Dashboard Development: Develop data and analytics applications that surface portfolio performance metrics and operational KPIs. Engineer internal dashboards used by investment professionals and senior leadership — designed to inform decision-making and continue driving a more proactive, data-driven approach to portfolio management.
• Portfolio Analysis: Conduct and structure analytical reports across fund and direct investments to assess portfolio performance, identify trends and risks, and generate insights that inform future investment strategy and portfolio construction decisions.

Required Technical Skills

• Excel: Advanced proficiency required.
• Finance & Accounting Basics: Understanding of the three financial statements, capital structures, performance metrics, financial ratios, and return metrics. Ability to understand how private equity and private credit work.
• Data Analysis: Comfortable working with large datasets and multiple databases simultaneously. Familiarity with BI/visualization tools (e.g., Tableau, Power BI) for report and dashboard generation.
• AI & Development Tools: Active user of AI-assisted workflows (Claude Code, Codex, Cursor, or equivalent); comfort leveraging LLMs as a core part of the build process is required. Working knowledge of Python and SQL — not required to be deeply technical, but must have used both in practice. Familiarity with API integration, scripting languages, or prior LLM application development is a plus.

Required Soft Skills

• Organization: Highly organized and detail-oriented; production quality matters.
• Problem-Solving & Big Picture Mindset: Approaches challenges with creativity and analytical thinking. Able to see beyond individual tasks to understand broader systems and processes.
• Operates in Ambiguity: Comfortable scoping, prioritizing, and building without a detailed playbook.
• Professionally mature for interaction with C-suite executives and institutional stakeholders.

Preferred Qualifications

• Sophomore, Junior, or Senior pursuing a degree in Finance, Economics, AI & Business Analytics, Data Analytics, Data Science, Computer Science, Information Systems, or a similarly quantitative field; expected graduation 2027, 2028, or 2029. A finance or economics background is required.
• Strong academic performance (3.5+ GPA).
• Tampa-based or able to reliably commute to the SMC Tampa office.
• Ability to work 15–20 hours per week during the school year and full-time out of the Tampa office between May and August.