At Sigma, we are rolling out a machine learning-enabled feature to extract entity relationships from news media. The purpose of this feature is to give financial crime investigators and compliance professionals deeper insight into people or companies that they are researching.

Sigma360 is a counterparty risk intelligence platform designed to reduce the risk and cost of compliance and improve the speed of business for our clients. Sigma is a Series A company ramping up for Series B funding and poised for significant growth. See more about Sigma here.

Learners will be involved in the end to end process of defining the machine learning model that meets business needs. The primary tasks the learner will be responsible for are:

• Validating outputs from machine learning models by identifying entities, identifying information and relationships in news media and comparing outputs to manually curated data.
• Gathering data necessary to support functional development of the Sigma360 application as it relates to news - e.g. frequency of certain data points available in our news articles.
• Calculating metrics (including precision and recall) to capture the performance of Sigma models over time.

Depending on the learner's skill and experience, additional tasks may include:

• Identifying and testing new machine learning models to support other functional areas of the application.
• Supporting other data tuning efforts such as those involved in measuring and improving entity resolution and search results.

Learners will report to Matt Monarch (Director, Data Products) throughout this project. Learners will be integrated into the existing data analyst team and will be supported as such - through other analysts, data engineers, product management, and dev management.