About Syntax Data

Syntax LLC is a financial data and technology company that codifies business models into a relational system we call Affinity Data™. Syntax operates through three segments: Affinity Data™, Syntax Direct™, and Syntax Indices™. Using its patented FIS® technology inspired by systems sciences, the Affinity Data™ segment offers the most comprehensive, granular, and accurate product line revenue data available on the market. The Syntax Direct™ segment then uses this abundance of data to facilitate the near instantaneous creation and ongoing management of boundless direct indexing solutions and rules-based equity portfolios through a fully automated platform. The Syntax Indices™ segment offers customized and proprietary indices, including core global benchmarks and micro- and macro-thematic, smart beta, defined outcome, and target volatility indices. These indices are foundational for a range of financial products, such as ETFs, UITs, and structured products.

About the Role

As ML Engineer you will be responsible for scaling Syntax’s set of classified public and private companies. Using our dataset of company descriptions, you will evaluate NLP algorithms and develop a ML model that will assign our proprietary classification labels to a high degree of accuracy. Working closely with our software engineering team, you will deploy your ML models to the cloud and develop APIs to provide access to the model from other systems. You will also have the opportunity to identify and apply novel applications of machine learning to other business processes.

Responsibilities

Designing, developing, and implementing AI/ML models for natural language processing (NLP) applications.
Working with large datasets, selecting appropriate algorithms and techniques, training or fine-tuning models to achieve optimal performance, and deploying and monitoring model performance in production.
Working in a collaborative team environment across product management, data engineering, and software engineering teams.

Requirements:

A bachelor’s degree or higher in computer science, data science or related discipline.
3+ years work experience in the machine learning field.
Prior professional experience working with NLP and machine learning.
Proficiency in programming languages commonly used in NLP, such as Python, and libraries/frameworks like TensorFlow, PyTorch, or spaCy and strong understanding of software engineering principles and best practices.
Strong knowledge of NLP techniques, including text data preprocessing (tokenization, stemming, and text normalization, etc.) and information extraction (summarization, and question answering, etc.).
Excellent problem-solving skills and the ability to work independently and collaboratively in a fast-paced, agile environment.
Strong communication skills and the ability to effectively articulate technical concepts to both technical and non-technical audiences.

Preferred Skills

Experience working in Finance or Financial Technology (FinTech).
Prior professional experience working with and deploying to cloud environments such as AWS, DigitalOcean, or equivalent.

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Machine Learning Engineer