This role is for one of the Weekday's clients
Salary range: Rs 1500000 - Rs 3500000 (ie INR 15-35 LPA)
Min Experience: 2 yearsLocation: BengaluruJobType: full-timeWe are seeking a highly analytical and detail-oriented
Data Scientist (Credit Risk)
to join our team and drive data-backed decision-making across our credit lifecycle. In this role, you will apply advanced data science techniques, machine learning models, and domain expertise in credit risk to build, validate, and optimize risk strategies that support responsible lending and portfolio growth. The ideal candidate combines strong technical skills in Python and ML with a deep understanding of credit risk concepts, regulatory expectations, and real-world business impact.
Requirements
Key Responsibilities
Required Skills & Qualifications
- Credit Risk Modeling & Analytics
- Develop, implement, and optimize credit risk models including scorecards, probability of default (PD), loss given default (LGD), exposure at default (EAD), and behavior/collection models.
- Conduct exploratory data analysis (EDA) to understand portfolio trends, credit performance, and customer behavior.
- Apply statistical and machine learning techniques to build robust predictive models for underwriting, fraud prevention, limit management, and risk segmentation.
- Monitor model performance, stability, and drift; recommend enhancements and recalibrations as needed.
- Data Science & Machine Learning
- Clean, preprocess, and engineer features using large, complex datasets from multiple structured and unstructured sources.
- Design and evaluate ML algorithms such as logistic regression, random forests, XGBoost, gradient boosting, SVM, and neural networks where appropriate.
- Perform model validation, cross-validation, hyperparameter tuning, back-testing, and sensitivity analysis.
- Leverage advanced analytics techniques such as clustering, time-series forecasting, optimization, and anomaly detection to support risk strategy development.
- Credit Strategy Development
- Partner with risk, product, and business teams to translate modeling insights into actionable credit policies and decision rules.
- Build analytical frameworks to evaluate credit strategies, pricing, portfolio performance, and loss mitigation initiatives.
- Assist in designing experiments (A/B tests) and measuring the impact of underwriting and credit line strategies.
- Data Engineering & Automation
- Work with engineering teams to ensure smooth data pipelines, model deployment, and scaling of analytical solutions.
- Build automated scripts and workflows in Python for model execution, monitoring, and reporting.
- Ensure best practices in data quality, documentation, reproducibility, and version control.
- Reporting, Compliance & Stakeholder Communication
- Prepare clear, concise presentations and reports tailored to technical and non-technical stakeholders.
- Support regulatory audits, model risk assessments, and documentation requirements.
- Communicate complex analytical findings with clarity, providing actionable insights and decision support.
- Bachelor's or Master's degree in Data Science, Statistics, Computer Science, Mathematics, Economics, or a related quantitative field.
- 2-7 years of hands-on experience in data science, preferably within banking, NBFCs, fintech, or risk analytics.
- Strong programming skills in Python (NumPy, pandas, scikit-learn, statsmodels).
- Solid understanding of machine learning algorithms, model development lifecycle, and performance metrics.
- Prior experience in credit risk modeling (scorecards, PD/LGD/EAD models) or credit analytics is mandatory.
- Proficiency in SQL and working with large datasets.
- Strong problem-solving ability, curiosity, and attention to detail.
- Excellent communication skills and ability to collaborate with cross-functional teams
