Quantamental Equity Research VP: AI-Driven Alpha

860 Morgan Stanley Svcs Canada Co; New York (NY); On-site; USD 140,000 - 250,000; Full time

Job summary

Morgan Stanley Equity Research in New York is seeking talented, self-motivated candidates at VP level for quantamental research, applying AI/ML to generate alpha and support equity calls across global stocks.

The role combines quantitative modeling with fundamental insights, backtesting and deploying models, and collaborating with sales and research teams to present findings to clients. Strong programming in Python and Q/kdb is essential, with UNIX/Linux experience required.

Qualifications

• Advanced degree in quantitative field; PhD preferred.
• Strong programming skills in Python and Q/kdb; 1-5 years development in UNIX/Linux.
• Experience with statistics learning methods and large datasets; knowledge of Deep Learning/LLMs a plus.

Responsibilities

• Combine quantitative processes with fundamental insights to forecast KPI, develop sector-specific signals, and build conviction on stock calls.
• Develop, backtest, and implement quant models to test the efficacy of various alternative data.
• Explore cutting edge tools such as genAI/LLMs to gain insights into new alpha.
• Publish leading research insight notes and present at industry conferences.
• Actively source new ideas and collaborate with other research teams; Partner with sales, providing quant proof statements to clients.
• Build tools to query, clean, analyze raw data through databases, and work with technology to standardize data hosting.

Skills

Python

Q/kdb

UNIX/Linux

Statistics

Deep Learning/LLMs

Communication

Team collaboration

Education

Advanced degree in quantitative field

PhD preferred

Job description

Morgan Stanley Equity Research in New York is seeking talented, self-motivated candidates at VP level for quantamental research, applying AI/ML to generate alpha and support equity calls across global stocks.

The role combines quantitative modeling with fundamental insights, backtesting and deploying models, and collaborating with sales and research teams to present findings to clients. Strong programming in Python and Q/kdb is essential, with UNIX/Linux experience required.