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Data Science Manager - Application Fraud

210k – 260kUnited StatesData ScienceRemote10+ YOE
Summary

Manage and mentor a team of data scientists building fraud detection models and financial risk products. Own end-to-end model development from data acquisition through production, while driving technical direction and cross-functional planning.

About the role

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.

Requirements

  • 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.
  • 3+ years of startup experience.
  • Excellent communicator and team player.
  • 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.
  • Experience writing production code and tests.
  • 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.

Technologies

  • Python 3
  • PostgreSQL
  • AWS infrastructure (EC2, S3, RDS, Redshift, etc.)

Nice-to-Haves

  • Familiarity with: identity solutions, fintech, or adjacent industries.
Skills
PythonPostgreSQLAWSMachine LearningStatisticsModel DevelopmentFeature EngineeringProduction CodeData AnalysisFraud Detection
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