Senior Quantitative Equity Research Analyst, AI Platform

About the role

We're looking for a Senior Quantitative Equity Research Analyst to lead the quantitative research behind our stock-selection models and help build a new generation of AI-powered equity research products.

You will be the primary quantitative research expert on a small, high-caliber team that brings together top-tier engineering and buy-side talent, including senior analysts with prior experience at firms such as Millennium, Point72, and KKR.

This is a hands-on individual contributor role with significant autonomy. You will cover investment research methodology, research prototypes, testing standards and model monitoring. Our engineering team will own production infrastructure and scalability, partnering with you to turn successful research into reliable systems.

You will operate as a research partner and lead the quantitative research behind High Quality Stocks, a long-only, factor-based stock-selection strategy designed around an approximately five-year ownership horizon. You will contribute to the development of both a quality framework and a valuation framework designed to identify high-quality businesses whose current prices offer attractive long-term return potential.

The quantitative model drives stock recommendations. Working closely with senior equity analysts, you will translate fundamental investment judgment into measurable hypotheses, systematic signals, ranking models, and decision rules. Analysts help shape and challenge the investment framework; you will own the methodology, empirical validation, and ongoing performance of our models.

What You'll Do

• Lead the quantitative research agenda behind High Quality Stocks.
• Improve and refine a quality framework and valuation framework for identifying attractive long-term equity investments.
• Partner with senior equity analysts to translate fundamental concepts and investment hypotheses into systematic signals and ranking models.
• Design rigorous back tests using point-in-time data, with appropriate controls for look-ahead bias, survivorship bias, overfitting, multiple testing, and regime dependence.
• Develop machine-learning models and frameworks to help improve performance of the strategy and discover new factors
• Evaluate factor performance across sectors, company sizes, market environments, time periods, and portfolio-construction approaches.
• Code research prototypes and apply statistical and machine-learning methods where they improve the quality or robustness of the investment process.
• Partner with equity data analysts and AI/ML engineers to validate financial data and turn successful research into scalable, production-quality systems.

What We're Looking For

• Minimum of 5 years of quantitative equity research experience, ideally within a long-only asset manager, fundamental quantitative team, long-biased strategy, systematic equity firm, or similar institutional environment.
• Direct experience researching long-only or long-biased equity strategies with medium- to long-term investment horizons.
• End-to-end ownership of quantitative research, from hypothesis and data construction through back testing, implementation, and performance evaluation.
• Strong fundamental equity knowledge, including financial statements, profitability, capital allocation, business quality, and valuation.
• Strong knowledge of factor research, statistics, back testing, portfolio construction, and machine learning.
• Strong Python skills and experience working with point-in-time fundamentals, estimates, market data, and other financial datasets.
• Ability to independently own quantitative research while communicating findings clearly to fundamental investors, engineers, and senior leadership.

Preferred Qualifications

• Experience researching quality, value, profitability, or other fundamental equity factors.
• Experience building stock-ranking models, screens, or model portfolios.
• Experience applying machine learning to cross-sectional equity research.
• Experience with LLM-powered research, financial-document analysis, or AI-assisted investment workflows.
• Experience building quantitative research or models used by portfolio managers, equity analysts, or individual investors.

Location & Perks

• Hybrid 3 days in office
• At CNBC Headquarters in Englewood Cliffs, NJ, you'll have access to great perks and amenities:
• Sweat it out -- Free onsite fitness center with state-of-the-art equipment, plus daily group classes
• Eat up -- Gourmet cafeteria with daily specials plus soup and salad bars
• Extras -- Dry cleaning, shoeshine, and sneak peeks
• Don't have a car? No problem! We offer free shuttle transportation to and from multiple locations in Manhattan, Brooklyn, Hoboken and Jersey City

In addition to these benefits, employees in this group will be joining at a time of meaningful and continued investment in data, technology, and product development, with the opportunity to contribute to a growing team within CNBC that is expected to scale significantly over time, offering meaningful exposure to senior leadership and the ability to influence how the platform evolves.

Additional Information

Versant

Media is committed to fair and equitable compensation practices. We include a good faith pay range for each position to comply with applicable state and local pay transparency laws and to promote equity across our organization. Actual compensation will be based on factors such as the candidate's skills, qualifications, experience, and location and may include additional forms of compensation and benefits such as health insurance, retirement plans, paid time off, etc.

Versant

Media is not accepting unsolicited assistance from search firms for this employment opportunity. All resumes submitted by search firms to any employee at VERSANT via-email, the Internet, or in any form and/or method without a valid written Statement of Work in place for this position from VERSANT's Talent Acquisition team will be deemed the sole property of VERSANT. No fee will be paid in the event the candidate is hired by VERSANT as a result of the referral or through other means.