ashby
Data Scientist, Full Stack
SentiLink
Remote, USA
Full-time
Junior · 2+ years experience
Remote
USD 180000 / year
Discovered 1 weeks ago
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Role:
As a Data Scientist at SentiLink, you will build our core products: models that identify fraudsters and also advance our growing suite of products in financial risk. As an experienced researcher you will be relied upon to be technically capable and the definitive owner of your respective domain. You will often work on projects with high visibility and impact that require deep domain understanding, critical thinking and strong technical abilities. You will work with teams across the company to research new types of fraud, develop new products, and provide analysis to drive sales and marketing. This is a full-stack data science role, involving model development, analysis, and writing production code. You should be interested in having end-to-end ownership and 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.)
Responsibilities:
- 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.
- Build foundational modeling to drive SentiLink’s expanding suite of Fraud and Financial Risk products.
- 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.
- Work with engineering, risk operations, and data acquisitions to access necessary data, maintain data quality, and support data access
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