VP/Head of Research, Merger Arbitrage

AllianceBernstein LP•New York, NY

13d•Onsite

About The Position

AB’s systematic merger arbitrage strategy sits within Multi-Asset & Hedge Fund Solutions, a team known for combining quantitative rigor with deep market expertise. The VP/Head of Research will report directly to the Head Portfolio Manager and serve as his primary research counterpart and deputy on the portfolio. This role involves close collaboration with technology and data engineering teams, focusing on research, signal development, and investment decision-making. The position also requires direct engagement with institutional clients and prospects, representing the strategy and contributing to business development.

Requirements

5+ years of investment management or quantitative research experience in equities.
Experience with quantitative research methods, including backtesting systematic strategies and evaluating signal performance.
Working proficiency in Python for research and data analysis purposes; comfort writing and maintaining production-quality scripts in an investment context.
Working proficiency in SQL for querying and managing structured datasets.
Excellent attention to detail and a strong commitment to data quality.
Strong communication skills; able to present investment views clearly and credibly to sophisticated institutional audiences.
Bachelor’s degree in a quantitative or finance-related discipline (Finance, Economics, Mathematics, Computer Science, Statistics, or similar).
Willingness to embrace and use AI to help with all facets of the job.

Nice To Haves

Experience with merger arbitrage or event-driven strategies a plus.
Familiarity with Merger Arbitrage.
Ability to analyze M&A transactions from announcement through close, including an understanding of typical deal timelines, deal structures, regulatory review processes, and break risk.
Experience with quantitative modeling and ML methods applied to event-driven or arbitrage investing (e.g., classification models, supervised learning for deal outcome prediction).
Exposure to systematic strategy development: factor construction, signal combination, and portfolio optimization.
Experience using AI tools to augment investment or research workflows; able to articulate clearly how AI has enhanced their work and to identify practical applications within a systematic strategy context.

Responsibilities

Identifying patterns in active and completed deals that can be systematically tested against historical data.
Designing and maintaining quantitative models and systematic signals to assess spread dynamics, deal break probability, and portfolio-level risk and return behavior.
Developing, backtesting, and evaluating new investment ideas using the team’s backtesting engine; owning the integrity of input data and the deal database.
Identifying and applying machine learning and statistical techniques to extract signals from deal, market, and alternative data.
Proactively identifying opportunities to leverage AI tools across the strategy — including data acquisition, deal monitoring, and research workflows — and helping embed those capabilities into the team’s day-to-day investment process.
Monitoring and assessing active deals across the M&A universe — tracking deal timelines, deal structure, regulatory and antitrust developments, and deal outcomes.
Periodically engaging with company management teams and deal advisors as needed to clarify deal timelines, assess regulatory risk, and confirm deal economics.
Tracking regulatory filings (HSR, SEC), antitrust proceedings, shareholder vote schedules, and deal timetables for all active positions.
Supporting the Head PM in position sizing, portfolio construction, and risk management decisions.
Assisting with trade building and portfolio monitoring.
Contributing to regular strategy performance reviews, providing attribution analysis and research-driven commentary on historical results.
Partnering with AB’s technology team to enhance research workflows, data pipelines, signal construction, and analytics.
Managing deal and market data across relational databases; maintaining data quality standards across all research inputs.
Identifying opportunities to expand the team’s data capabilities through alternative or third-party data sources.
Attending meetings with institutional clients and prospects to present the strategy, discuss current positioning, and present research findings.
Contributing to the preparation of investor materials, performance commentary, research publications and the strategy’s quarterly investment letter.
Serving as a credible and articulate spokesperson for the strategy in client interactions and external forums.