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Prism Data is building the future of credit scoring with modern technology and data science. Prism’s platform enables banks and other lenders to utilize automated cash flow underwriting—analyzing detailed banking history alongside traditional credit history in an instant—to improve financial decisions, expand credit approvals, lower losses, and more. Prism’s API-based, no-PII platform translates raw transaction data from any source into actionable insights and scores including the market-leading CashScore® for credit risk management. Prism works with some of the biggest banks and fastest growing fintechs in the country and Prism’s technology has been used to power billions of dollars worth of credit originations and to improve millions of credit decisions. Prism is well-funded and backed by large financial institutions like Synchrony and Citi, as well as top VCs like Obvious Ventures, Core Innovation Capital, and Valar Ventures.
Job Responsibility:
Contribute to Prism’s engineering culture by regularly mentoring other engineers and facilitating knowledge sharing, learning, and continuous improvement
Advance an architectural runway to support product extensibility and scalability while balancing sustainability
Architect and lead the enhancement of production ML-serving infrastructure, including continuous advancement of MLOps capabilities
Design, build, and operate enterprise-grade APIs and other platform services, and lead strategic co-development opportunities with key partners and data providers
Drive technical direction across platform and model-serving layers, ensuring observability, security, and performance at scale
Partner cross-functionally with product, legal, and go-to-market teams to cohesively develop new capabilities that expand cash flow analytics use cases
Requirements:
Software engineering experience, ideally in a high-growth or early stage startup
Strong expertise in modern software practices and technologies, with ability to quickly adapt to Prism’s services stack
Deep hands-on experience with Python, Kubernetes, and AWS, with specific experience supporting machine-learning models from prototyping through production operations
Proactive bias-to-solve technical problems and navigate complex design decisions
Excellent communication skills with the ability to bridge gaps between technical teams and non-technical stakeholders
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