AI Research Scientist

Required Qualifications

• · Bachelor's degree in Computer Science, Data Science, Machine Learning, Statistics, Mathematics, Engineering, or a related quantitative field.
• · 2+ years of experience building and deploying machine learning or AI solutions in production.
• · Strong Python skills and experience writing production-grade, well-tested code.
• · Solid grounding in machine learning, statistical modeling, and predictive analytics.
• · Hands-on Generative AI experience, including LLMs, embeddings, retrieval systems, prompt engineering, and LLM evaluation.
• · Experience designing agentic or multi-agent systems using tool calling, API integrations, Model Context Protocol (MCP), and orchestration frameworks (e.g., LangGraph, LlamaIndex, or similar).
• · Clear communication skills, including the ability to explain technical trade-offs to non-technical stakeholders.

Preferred Qualifications

• · Master's degree or PhD in Computer Science, Data Science, Machine Learning, Statistics, Mathematics, Engineering, or a related quantitative field.
• · Experience applying AI or quantitative methods in asset management, financial services, fintech, or capital markets.
• · Working knowledge of investment research, portfolio management, financial modeling, or risk management.
• · Experience with financial text data such as company filings, earnings call transcripts, news, and research reports.
• · Familiarity with cloud platforms (AWS, Azure, or GCP), distributed computing, vector databases, and MLOps practices such as CI/CD, containerization, and experiment tracking.
• · A track record of turning research-stage AI ideas into scalable business applications.

About AB

Responsibilities

• · Build Generative AI applications. Design and deploy LLM-powered systems, including retrieval-augmented generation (RAG), intelligent agents, and multi-step agentic workflows that automate complex research and analytical tasks.
• · Develop quantitative and predictive models. Create forecasting, ranking, and predictive models from structured and unstructured data to support investment research, portfolio management, and risk analysis.
• · Take solutions to production. Build scalable software and data pipelines, and set up evaluation, monitoring, observability, and governance so models stay reliable and deliver measurable business value.
• · Partner with the business. Work with investment, operation, and distribution teams to turn business problems into practical, well-scoped AI solutions.
• · Advance the firm's AI capabilities. Evaluate emerging research and tools, prototype promising approaches, and lead their adoption across the organization.
• What We’re Looking For:
• · Bachelor's degree in Computer Science, Data Science, Machine Learning, Statistics, Mathematics, Engineering, or a related quantitative field.
• · 2+ years of experience building and deploying machine learning or AI solutions in production.
• · Strong Python skills and experience writing production-grade, well-tested code.
• · Solid grounding in machine learning, statistical modeling, and predictive analytics.
• · Hands-on Generative AI experience, including LLMs, embeddings, retrieval systems, prompt engineering, and LLM evaluation.
• · Experience designing agentic or multi-agent systems using tool calling, API integrations, Model Context Protocol (MCP), and orchestration frameworks (e.g., LangGraph, LlamaIndex, or similar).
• · Clear communication skills, including the ability to explain technical trade-offs to non-technical stakeholders.
• · Master's degree or PhD in Computer Science, Data Science, Machine Learning, Statistics, Mathematics, Engineering, or a related quantitative field.
• · Experience applying AI or quantitative methods in asset management, financial services, fintech, or capital markets.
• · Working knowledge of investment research, portfolio management, financial modeling, or risk management.
• · Experience with financial text data such as company filings, earnings call transcripts, news, and research reports.
• · Familiarity with cloud platforms (AWS, Azure, or GCP), distributed computing, vector databases, and MLOps practices such as CI/CD, containerization, and experiment tracking.
• · A track record of turning research-stage AI ideas into scalable business applications.