Job Description :

- 3+ years exp on Python, ML and Banking model development.

- Interact with the client to understand their requirements and communicate / brainstorm solutions, model Development

- Design, build, and implement credit risk model.

- Contribute to how analytical approach is structured for specification of analysis.

- Contribute insights from conclusions of analysis that integrate with initial hypothesis and business objective and independently address complex problems.

- 3+ years exp on ML/Python (predictive modelling).

- Design, implement, test, deploy and maintain innovative data and machine learning solutions to accelerate our business.

- Create experiments and prototype implementations of new learning algorithms and prediction techniques.

- Collaborate with product managers, and stockholders to design and implement software solutions for science problems.

- Use machine learning best practices to ensure a high standard of quality for all of the team deliverables.

- Has experience working on unstructured data (text) : Text cleaning, TFIDF, text vectorization.

- Hands-on experience with IFRS 9 models and regulations.

- Data Analysis : Analyze large datasets to identify trends and risk factors, ensuring data quality and integrity.

- Statistical Analysis : Utilize advanced statistical methods to build robust models, leveraging expertise in R programming.

- Collaboration : Work closely with data scientists, business analysts, and other stakeholders to align models with business needs.

- Continuous Improvement : Stay updated with the latest methodologies and tools in credit risk modeling and R programming.

Role Details :

- Role : Data Science & Machine Learning - Other

- Industry Type : Analytics / KPO / Research

- Department : Data Science & Analytics

- Employment Type : Full Time, Permanent

- Role Category : Data Science & Machine Learning

Education :

- UG : Any Graduate

Key Skills :

- Python, Predictive Modeling, Model Risk, risk modeling, Natural Language Processing, Regression Modeling, Machine Learning, Fraud detection, Deep Learning, Scikit-Learn, Numpy, Model Development, SQL, Data Science, Fraud Monitoring, R, Xgboost, Pandas, Statistical Modeling, Scorecard.