Sr. Data Scientist

EDGE (www.edgescore.com) is a B2B fintech on a mission to expand credit access for consumers historically unserved and underserved by the reports and scores of traditional credit bureaus. We’re doing this with risk analytics based on complementary alternative data that reveals a more complete financial picture.

Founded in 2021, Edge is part of a larger family of fintech brands underneath our parent company, NinjaHoldings (www.ninjaholding.com). NinjaHoldings’ brands include CreditNinja, a nationwide online consumer lending business established in 2017, and NinjaCard, a neobanking platform focused on consumers in the emerging credit space.

The EDGE business is at an inflection point where we’ve proven our platform’s value with early adopters, and we’re ready to go full-throttle into our target markets. Elevating every aspect of our marketing efforts is critical in this planned ramp-up.

Job Summary

We're looking for a Senior Data Scientist with the initiative and technical depth to not only propose ideas—but build them. You should be someone who thrives on ownership: hypothesizing features and improvements, prototyping them end-to-end, validating them with real data, and partnering with engineers to scale them in production.

This role sits at the intersection of data science, product thinking, and engineering. You will lead the design of analytics-driven features and systems that directly shape our products and impact real-world outcomes.

Key Responsibilities:

Analytics Product End-to-End Development

• Formulate and test hypotheses for new features, scoring improvements, and system enhancements
• Rapidly prototype models and data workflows to validate ideas
• Partner with engineering to scale successful prototypes into robust, production-grade systems

Modeling & Research

• Design and train ML models for credit risk, fraud, and other fintech use cases
• Perform deep dives and exploratory analysis to uncover actionable insights and drive product improvements
• Define and develop meaningful feature sets using structured and semi-structured data

Analytics Engineering

• Collaborate with engineering to build and maintain gRPC/RESTful services to serve models and analytics
• Operate in a modern containerized environment using Docker and Kubernetes
• Work with AWS infrastructure (EKS, S3, RDS) to deploy and monitor solutions

Collaboration & Technical Leadership

• Collaborate cross-functionally with product, engineering, and risk
• Provide technical mentorship and contribute to standards and best practices
• Participate in architectural decisions and roadmap planning

Requirements:

• Experience in consumer lending, credit risk modeling, or financial services
• 4+ years of experience in data science, analytics engineering, or backend ML systems
• Advanced proficiency in Python, including libraries like Pandas, NumPy, and scikit-learn
• Strong SQL skills and deep familiarity with relational databases (PostgreSQL or similar)
• Experience with model lifecycle: from prototyping to validation to deployment
• Comfort working with cloud-native infrastructure (Docker, Kubernetes, AWS)
• Experience building APIs (gRPC and/or REST) for real-time model inference or data delivery
• A strong sense of ownership and a bias for action—if you see a problem, you solve it

Additional Pluses, but not requirements:

• Familiarity with real-time decision systems and model serving

Benefits:

• Competitive salary and benefits package
• Fun, fast-paced work environment
• Dynamic start-up culture
• Ability to make an immediate impact in a growth stage company
• Convenient downtown Chicago office located in the heart of the city
• Equal opportunity employer

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