Data Science Manager - Application Fraud

As a Data Science Manager for 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.

Technologies: Python 3, PostgreSQL, and AWS infrastructure (EC2, S3, RDS, Redshift, etc.)

- 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.

- 10+ years relevant work experience & relevant Masters or 7+ years & relevant PhD.

- 2-5 years experience directly managing a team of data scientists, ideally in a startup environment.

- Proven track record of solving complex / high profile business problems with DS / ML solutions.

- Experience in communicating outcomes / progress to senior management / stakeholders.

- Very strong in “end to end” DS development: Planning, fleshing out success criteria / metrics, getting buy-in, developing the solution, delivering the solution (prod / deck / strategy doc / etc).

- Strong practical ML / Stats knowledge, i.e. can easily employ the suite of standard ML / stats tools to quickly scope out solutions, and double down where needed. Experience with SOTA ML solutions is a plus.

- Interest in developing deep domain expertise for product-focused work: a background in fraud is not required, but willingness to learn is.

- Detail oriented and thoughtful—someone we can rely on to make business-changing decisions.

- Thrive in a fast paced environment characterized by the need to solve extremely varied, high impact, open ended problems.

- Bonus for familiarity with: identity solutions, fintech, or adjacent industries.

- Candidates must be legally authorized to work in the United States and must live in the United States.