Associate, AI Solutions & Quantitative Strategist

Primary Responsibilities

• Drive the development and execution of Hybrid’s AI roadmap in close partnership with Hybrid leadership, the Hybrid research/investment team and Apollo Global Investment Insight Quant Team.
• Act as a transformational agent across the Hybrid platform, identifying high-impact AI use cases across sourcing, diligence, underwriting, portfolio management, reporting and internal operations.
• Encourage adoption and engagement across investment teams by conducting teach-ins, training sessions, workshops and targeted working sessions to embed AI tools into day-to-day workflows.
• Partner with Apollo engineering and data teams to scope, prioritize, design, test and deploy new AI-enabled tools and enhancements tailored to Apollo Hybrid’s team needs.
• Evaluate and pilot external AI vendors and service providers, including conducting structured testing, cost-benefit analysis, security/compliance coordination and performance assessments.
• Develop proof-of-concepts and practical prototypes to demonstrate value and accelerate buy-in from stakeholders.
• Serve as a liaison between Hybrid investment professionals, Apollo Global Investment Insight Quant Team and technology teams to translate business needs into technical requirements and ensure solutions are practical and scalable.
• Cook up across Apollo functions (legal, compliance, risk, information security, finance, human capital) to ensure AI initiatives are implemented in alignment with firm-wide standards and governance.
• Stay current on developments in AI, machine learning and emerging technologies, bringing relevant insights and opportunities to the Apollo Hybrid platform.

Key Areas of Focus

• Investment research automation
• Screener / Portfolio monitoring tools
• Investment team workflow / process automation

Qualifications & Experience

• Bachelor’s degree with a strong record of academic achievement; advanced degree a plus.
• 3+ years of experience in quantitative finance, data engineering, or applied AI/ML in an investment context.
• Familiarity with financial data APIs (Bloomberg, ICE, Refinitiv) and structured/unstructured data pipelines.
• Familiarity with credit markets — leveraged loans, high yield bonds, or structured credit — sufficient to understand the investment workflow including sourcing, diligence, underwriting and portfolio management and ability to build relevant tools.
• Experience with credit analysis workflows: capital structure modeling, comparable company analysis, scenario analysis.
• Experience building and deploying LLM-based applications (prompt engineering, RAG, agent frameworks such as LangChain or similar).
• Familiarity with vector databases, fine-tuning workflows, or evaluation frameworks for LLM outputs.
• Ability to work directly with investment professionals to translate qualitative use cases into working quantitative tools.
• High attention to detail and strong communication skills — outputs will be used in live investment decisions.