Senior Data Scientist

Scienaptic is on a mission to increase credit availability by transforming technology used in credit decisioning. Over 150 years of credit experience is embedded in Scienaptic's AI native credit decision platform. Our clients across banks, credit unions, fintech, and other lenders use the platform to constantly improve the quality of underwriting decisions. This enables them to say ‘yes’ to borrowers more often and faster.

The Data Scientist role at Scienaptic will enable you to be at the forefront of latest cutting- edge technology and create a significant and visible business impact for Scienaptic.

You will be working with some of the best-in-class Engineers, Data Scientists, and Business Analytics Consultants in an environment which will encourage you to contribute widely to functional and technological aspects without worrying about conventional job silos.

Job Responsibilities:

Design, develop, and deploy credit risk models and strategies, Lead the development of decision systems for SMB lending. Work closely with client teams to define problem statements, validate solutions, and drive adoption. Present model/strategy outcomes and recommendations to internal and external stakeholders.

Work closely with internal teams including Client Engagement, Engineering and Product to ensure smooth integration and deployment.

Collaborate with product, engineering, and analytics teams to integrate our product into client workflows and platforms.

Develop repeatable frameworks and toolkits to accelerate deployment and monitoring across clients.

Requirements:

4+ years of experience in data science and machine learning with prior experience in

credit risk, fintech, or financial services domains.

Prior experience working in SMB lending

Prior experience working in SMB lending in the US market (good to have)

Experience working with cross-functional teams including product, business, and engineering.

Strong client-facing and communication skills - ability to translate business goals into analytical frameworks.

Hands-on experience with Python

Bachelor/master’s degree in a quantitative discipline (e.g., Statistics, Economics, Computer Science, Mathematics).