Applied Data Scientist - Credit Risk Modeling

Applied Data Scientist - Credit : 4-7 Overview :

We are looking for a passionate and highly skilled Applied Data Scientist - Credit Risk with a strong foundation in machine learning, statistical modelling, and real-world problem solving within the BFSI and lending domain.

The ideal candidate will have 4- 7 years of hands-on experience in building, fine-tuning, and deploying end-to-end data science solutions, ranging from traditional risk scorecards to advanced machine learning models. You will be responsible for translating complex credit and portfolio risk challenges into scalable, production-grade AI/ML solutions that power our next-generation lending products and decision engines.

Key Responsibilities :

Model Development & Research :

- Credit Risk Modeling : Design and develop end-to-end credit risk models, including application scorecards, behavioral model development, and portfolio risk modeling.

- Supervised Machine Learning : Apply advanced supervised machine learning techniques alongside traditional statistical frameworks like Logistic Regression, Generalized Linear Models (GLM), and XGBoost.

- Unsupervised Learning : Utilize unsupervised learning techniques like PCA (Principal Component Analysis) for dimensionality reduction and K-means customer clustering to identify risk segments, fraud vectors, and behavioral patterns.

- Regulatory Compliance : Develop and implement regulatory risk modeling solutions aligned with international standards such as BASEL-2 and IFRS9 frameworks.

- Risk Metrics Estimation : Own the development of core risk parameters, including Probability of Default (PD), Loss Given Default (LGD), and Expected Credit Loss (ECL).

- Performance Benchmarking : Benchmark and continuously improve model performance using appropriate evaluation metrics and experimentation frameworks.

Data Handling & Feature Engineering :

- Large-Scale Data Processing : Work with large-scale structured and unstructured datasets to build robust data pipelines, conduct exploratory data analysis, and engineer high-quality features.

- Credit Bureau Integration : Leverage deep familiarity with Credit Bureau data to extract, preprocess, and incorporate bureau features into predictive workflows.

- Data Quality Assurance : Ensure rigorous data preprocessing, augmentation, and validation to maximize model accuracy and generalization.

Algorithm Design & Optimisation :

- Production Optimization : Collaborate with cross-functional teams to design and implement scalable algorithms optimised for production environments.

- Model Explainability : Ensure risk algorithms conform to high standards of transparency, interpretability, and statistical soundness.

Cross-Team Collaboration :

- Business Alignment : Partner closely with product, engineering, and business teams to align AI/ML solutions with organizational goals.

- Scoping Requirements : Translate ambiguous business and portfolio problems into well-scoped data science problem statements with clear success criteria.

Research & Innovation :

- Continuous Learning : Stay current with state-of-the-art advancements in machine learning.

- Methodology Adoption : Evaluate and adopt relevant new techniques into the team's workflow.

- Knowledge Sharing : Contribute to internal knowledge sharing and, where applicable, to external publications, technical blogs, or patents.

Mentoring & Knowledge Sharing :

- Team Guidance : Mentor junior data scientists and ML engineers, providing guidance on modelling approaches, code quality, and production readiness.

Education Qualifications :

- Bachelor's or Master's degree from a premier institution in Computer Science, Statistics, Mathematics, Data Science, or a related quantitative field.

Experience :

- 4- 7 years of professional experience in applied data science, machine learning engineering, or risk analytics specifically within the BFSI and lending domain.

- Proven track record of developing credit risk scorecards, behavioral models, and regulatory frameworks (BASEL-2 / IFRS9 / ECL / PD / LGD).

- Demonstrated experience implementing supervised machine learning techniques and unsupervised learning techniques (e.g., PCA, K-means customer clustering).

- Strong understanding of statistical fundamentals and neural network fundamentals.

- Generative AI : Experience or familiarity with Agentic AI frameworks and Retrieval-Augmented Generation (RAG) architectures.

- Deep Learning : Hands-on experience applying deep learning techniques to financial services or credit risk use cases.

- Advanced LLM Frameworks : Familiarity with prompt engineering, RLHF, and LLM evaluation frameworks.

- Governance : Contributions to open-source ML projects, published research, or active participation in responsible AI and strict model governance practices within regulated industries.

Technical Skills :

- Core Languages : Python, pandas.

- Data Engineering : SQL, pandas, Spark / PySpark for large-scale data processing.

- ML Frameworks & Libraries : Scikit-learn, XGBoost, TensorFlow, PyTorch.

- Core Modeling Techniques : Supervised Machine Learning (Logistic Regression, GLM, XGBoost), Unsupervised Machine Learning (PCA, K-means customer clustering).

(ref:hirist.tech)