AI Research Scientist

AllianceBernstein•Nashville, TN

21h•Onsite

About The Position

The Data Science team partners closely with investment professionals, research analysts, and technology teams to develop and deploy advanced AI, machine learning, and quantitative solutions across the firm. The team combines expertise in data science, software engineering, financial modeling, and artificial intelligence to build innovative tools that enhance research productivity, investment decision-making, and operational efficiency. The role offers the opportunity to work at the intersection of cutting-edge AI technologies and real-world financial applications.

Requirements

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.

Nice To Haves

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.

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.