Head of Machine Learning - Application Fraud

As Head of Machine Learning, Application Fraud at SentiLink, you will directly manage a team of talented data scientists that build our suite of products: models that identify fraudsters and also advance our growing suite of products in financial risk. In this capacity, you will be relied upon to be a technical leader, mentor, and owner of your respective domain, both for your own team and the organization. Utilize your abilities as a strong data scientist to mentor your team, probe their understanding and challenge them to do their best work. Your team will often work on projects with high visibility and impact that require deep domain understanding, critical thinking and strong technical abilities.

This role requires managing full-stack data scientists, whose work involves model development, analysis, and writing production code. You yourself should share that same skillset and be interested in having end-to-end ownership in a fast-moving environment where deep domain understanding drives development and unusual insights drive our competitive advantage rather than optimization of new machine learning methodologies.

Responsibilities:

- Directly manage a team of highly skilled data scientists. Start from 2-3 and grow to 5-6.

- Act as a technical mentor that can dive deep and provide detailed direction.

- Lead planning, resourcing and communications with senior leadership, product and engineering teams.

- Develop strong business intuition and guide your team to deliver performant DS solutions on aggressive timelines.

- Develop and maintain SentiLink’s fraud detection models through the full model development lifespan: from data acquisition decisions through featurization, focusing labeling resources, model training, experimentation, productionalization, and monitoring.

- Research new types of fraud and develop new SentiLink products around identity verification.

- Achieve success by researching / developing through iteration, integration of new data sources and inventive feature engineering.

- Write production-ready code that can be relied on for real-time decision making by our partners.

- Design, perform, and present analyses that will inform data acquisition, product development, risk operations priorities, marketing, and sales efforts.