Research Scientist (Recommendation Engines)

Stabile Search • New York, NY

3d • Hybrid

About The Position

The role involves joining an ML-driven alpha research group at a prestigious quantitative hedge fund in New York City. The primary focus is to apply ranking, recommendation, and sequence modeling techniques to financial data. This includes ranking and weighting external stock pickers to identify signal from noise and developing shorter-horizon forecasting signals for stock returns for the firm's intraday trading platform. The position requires owning the work end-to-end, from research and development to production deployment, and working with graph embeddings, ranking/recommendation algorithms, and sequence models on time series data. Successful candidates will see their models drive live trading in real time.

Requirements

• 2 to 6 years of experience building recommendation, ranking, or personalization systems.
• Hands-on experience with graph embeddings.
• Experience with sequence modeling, such as predicting behavior from user activity history or other time series inputs.
• A track record of deploying models to production and owning them front to back.
• A broad ML generalist comfortable working across diverse datasets.
• Product-minded, with experience optimizing a product or platform for a real audience.

Nice To Haves

• Ideally, experience at a large consumer technology company.
• Ideally, you have led a recommendation system build.
• Ideally, your core work measurably improved user engagement or personalization.
• Prior experience in finance is not required.

Responsibilities

• Apply ranking, recommendation, and sequence modeling techniques to financial data.
• Rank and weight a large network of external stock pickers to separate signal from noise.
• Turn stock picker views into predictive features for the firm's core.
• Build shorter-horizon forecasting signals for stock returns (multiple hours to overnight) for the firm's intraday trading platform.
• Own work end to end, from alpha research and model development through to production deployment.
• Work with graph embeddings, ranking and recommendation algorithms, and sequence models on time series data.
• See the results of models in real time as they drive live trading.

Benefits

• Total compensation budget in the range of $600,000 to $900,000, depending on experience.
• Opportunities for growth, learning and career advancement.
• Collaborative, research-driven culture.
• Resources to take ideas from research all the way to production at scale.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed