Machine Learning Engineer, Knowledge Graph Intelligence

A Career with point72’s Surveillance team

Point72’s Surveillance team sets the industry standard for intelligence-driven surveillance by proactively identifying, monitoring, and assessing various sources of compliance risk using proprietary tools and specialized tradecraft.

We support senior management by providing strategic assessments, actionable recommendations, and real-time escalations.

At Point72, members of the Surveillance team conduct integrated trade and communication surveillance and collaborate to turn information into intelligence for our internal customers.

The team also monitors employee activity for evidence of violations of applicable federal securities laws, internal compliance policies and procedures, and relevant rules and regulations enforced by the SEC, FINRA, and other organizations.

What you’ll do

As a Machine Learning Engineer - Applied Scientist you will play a critical role in developing algorithmic solutions and models for production-ready applications that support our front office investment professionals.

You will specialize in natural language processing (NLP) solutions that extract insights from unstructured text data, with additional capabilities in predictive modeling, clustering, and time series analysis.

You will manage all aspects of the research process including methodology selection, data collection and analysis, implementation and testing, prototyping, and performance evaluation.

You will apply, adapt, and extend existing results in the broad field of NLP, while also conducting novel research as required.

Specifically, you will:

• Contribute to projects across various machine learning (ML) disciplines, including NLP, unstructured data analysis, predictive modeling, and classic machine learning.
• Implement GenAI solutions, utilize ML infrastructure, and contribute to modeling, data preparation, optimization, and performance enhancements.
• Work with sparse data and apply techniques to improve model accuracy and generalization.
• Conduct data evaluation, including data preprocessing, feature engineering, and model performance assessment.
• Collaborate cross-functionally with data engineers, software developers, and product teams to integrate models into production systems.
• Stay up to date with the latest advancements in natural language processing and machine learning, applying new techniques as needed.