🛠 Tech Stack

💼 About This Role

You'll develop machine learning models for risk scoring, fraud detection, and collections optimization using large-scale alternative data. Your work directly improves financial inclusion for underbanked populations in a mission-driven fintech environment.

🎯 What You'll Do

• Develop and deploy ML models for risk scoring, fraud detection, and collections
• Build automated model monitoring systems for performance and drift tracking
• Design scalable data pipelines from ingestion to model output delivery
• Collaborate with compliance, model risk teams, and cross-functional stakeholders

📋 Requirements

• Master’s or PhD in quantitative field (Statistics, CS, Engineering, etc.)
• 3+ years hands-on data science and predictive modeling experience
• Proficiency in Python and SQL for large-scale data processing
• Experience with machine learning techniques like gradient boosting and neural networks

✨ Nice to Have

• Experience with cloud platforms like AWS (EMR, SageMaker)
• Familiarity with big data ecosystems (Spark, Hive)
• Experience in fast-paced, agile environments

🎁 Benefits & Perks

• 💰 Competitive compensation aligned with experience
• 🌍 High-impact financial inclusion work
• 📊 Exposure to alternative data and advanced ML systems
• 📈 Career growth in a global, mission-driven organization
• 🤝 Collaborative and inclusive work culture

📨 Hiring Process

Estimated timeline: 2-4 weeks · AI estimate

1. 1Recruiter Call· 30 min
2. 2Technical Screening· 60 min
3. 3Hiring Manager Interview· 60 min