Data Scientist (Fintech)

TL;DR

Data Scientist (Fintech): Designing and deploying high-precision Credit Risk and Fraud models with an accent on automated lending systems and AI research. Focus on bridging traditional risk modeling with LLM applications to redefine the credit lifecycle.

Location: Colombia (implied by regulatory requirements)

Company

hirify.global is a leading Colombian financial platform providing banking solutions, deposits, and Buy Now, Pay Later services to millions of customers.

What you will do

• Participate in the full ML lifecycle from business definition to design, implementation, and monitoring of risk models.
• Collaborate with Engineering to design and optimize data pipelines for scalable production deployment.
• Design and execute A/B tests and holdouts to validate model impact on business metrics.
• Research and prototype AI/LLM techniques for document processing, fraud recognition, and credit analysis.
• Translate complex data insights into actionable strategies for Risk, Fraud, and Product teams.

Requirements

• 3+ years of technical experience in Data Science specifically for Credit Risk or Fraud.
• Bachelor's degree in a quantitative field such as Mathematics, Statistics, Physics, or Computer Science.
• Proficiency in Python (pandas, scikit-learn, xgboost) and SQL.
• Proven track record of deploying supervised models for regression and classification into live environments.
• Deep understanding of Colombian regulatory frameworks, including provisioning and SARLAFT.
• Experience with model interpretability techniques like SHAP and LIME.

Culture & Benefits

• Meaningful company ownership through equity.
• Competitive salary and comprehensive benefits package.
• High-impact role in a rapidly scaling fintech environment.
• Opportunity to work with a world-class, international team of experts.

Hiring process

• People Interview: Initial conversation with a recruiter or hiring manager.
• Initial Technical Interview: Deep dive into skills and problem-solving approach.
• Take Home Challenge: Technical exercise with a 5-hour expected time investment.
• Challenge Review: Technical discussion with a Data Scientist and the DS Lead.
• Co-Founder Interview: Final alignment on cultural fit and expectations.