Data Scientist

• Build, validate, and deploy real -time credit risk and fraud detection models to
  support underwriting decisions
  
• Work with large -scale, imbalanced datasets to extract meaningful risk insights
  
• Develop thin -file / new -to -credit models using alternative data sources (mobile,
  transactional, behavioural signals)
  
• Optimize credit underwriting strategies including approval rates, credit limits,
  and pricing decisions
  
• Design and execute A/B experiments to improve portfolio performance while
  maintaining risk thresholds
  
• Continuously monitor model performance, stability, and drift; recommend
  recalibration strategies
  
• Collaborate with Product, Engineering, and Growth teams to integrate models
  into scalable APIs and decision systems
  
• Build and maintain automated data pipelines and model retraining workflows
  
• Balance risk vs. growth trade -offs, aligning with business objectives in a fintech
  lending environment
  
• Translate complex analytical outputs into clear business recommendations for
  stakeholders
  

Requirements

• 2–5 years of experience in credit risk analytics, lending analytics, or fintech data
  science.
  
• Master’s degree in a quantitative field such as Statistics, Computer Science,
  Economics, Applied Mathematics, or related discipline.
  
• Hands -on experience in statistical modeling, machine learning, and predictive
  analytics.
  
• Strong ability to work with messy, real -world datasets (incomplete, noisy, biased)
  and large -scale data processing.
  
• Experience in small business lending, fintech, or alternative credit ecosystems.
  
• Familiarity with model governance, validation frameworks, and explainability
  techniques (e.g., SHAP).
  
• Exposure to cloud environments (AWS) and modern data engineering workflows.
  

• SQL & Snowflake for data extraction, transformation, and large -scale querying.
  
• Python for modeling, automation, and data analysis (Pandas, NumPy, Scikit
  learn, etc.).
  
• Tableau (or similar BI tools) for data visualization and stakeholder reporting.
  
• Understanding of ML lifecycle (training, validation, deployment, monitoring). 
  
• Exposure to API integration and production -level model deployment.
  

Benefits