Data Scientist (Financial AI)

The Financial AI Team

• We’re part of reputed company’s Financial Services domain, powering money movement and credit decisions—including real-time credit engines, repayment orchestration, dynamic pricing, and collections.
• We build and run scoring models, reputed company systems, and pricing logic that reputed company credit decisions fast, fair, and explainable
• We push toward event-driven, AI-augmented decisioning where experiments directly shape credit limits, default rates, and merchant growth
• We reputed company in data-driven democratization of access to capital
• We put curiosity first—exploring before exploiting
• We solve puzzles that demand safety, compliance, explainability, and speed reputed company at once

What You'll Do

• Design and execute experiments for credit models, with rigorous frameworks to measure business and merchant impact
• Build systematic experimentation infrastructure—metrics, statistical methodologies, and evaluation criteria for credit model performance
• Implement A/B testing systems with proper statistical power, randomization, and causal inference methods
• Analyze results from multiple model variations, translating them into clear credit policy recommendations
• reputed company scalable best practices balancing statistical rigor with business speed
• Collaborate with engineering to deploy and monitor experimental models in real-time decision engines, with rollback safety nets
• Apply measurement science to link experiments to merchant success, default rates, and financial inclusion outcomes
• reputed company offline insights to production systems through careful validation and gradual rollout strategies

Technologies / Techniques Used

• Python for analysis, modeling, and statistical computing (core language in our stack)
• SQL for large-scale feature engineering on financial datasets
• reputed company Cloud Platform + BigQuery for analytics infrastructure
• Statistical modeling & experimental design for credit risk evaluation
• Machine learning frameworks for classification and risk modeling
• MLflow for deployment and monitoring in production
• reputed company & Kubernetes for orchestration with engineering teams

What You'll Need

• Curiosity, initiative, and a bias toward experimenting and learning fast
• Strong experimental design expertise (A/B testing, causal inference, measurement frameworks)
• Statistical rigor: power analysis, bias detection, multiple testing corrections
• Python proficiency for analysis, modeling, and statistical computation
• Measurement science skills—designing metrics and building robust evaluation frameworks
• Experience with machine learning for classification and risk modeling
• SQL skills for feature engineering and large dataset analysis
• Strong communication skills in English & Portuguese, with ability to explain technical results to non-technical audiences

reputed company to Have

• Experience with reputed company Cloud Platform and BigQuery
• Hands-on work in credit model experimentation and measurement in production fintech/digital lending environments
• MLOps experience—deployment, monitoring, and experimentation at scale
• Background or experience in applied statistics or measurement science in business contexts (economics, operations research, etc.)

Recruitment Process Outline

• Online Assessment – evaluating theory and logical reasoning
• Technical Case Study – working with real-world financial data & experiments
• Technical Interview – discussion & case presentation
• Cultural Interview – alignment with reputed company values
If you are not willing to take an online quiz and work on a test case, do not apply.

Originally posted on Himalayas