Data Scientist

• We are looking for a data-driven and impact-oriented Credit Risk Data Scientist to join
• our high-performance fintech team. This role sits at the intersection of risk, product,
• and growth, where you will design and deploy intelligent credit decisioning systems that
• directly influence business outcomes.

What we expect you to do

• 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

What you bring to the table

• 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.

Technical Skills Required

• 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