Large data management, feature engineering, strong hands on in EDA and feature Engineering using bureau data in banking domain
Create Python classes and functions to automate EDA and Feature Transformation process
Good to have - Design, implement, and optimize sophisticated machine learning models in Banking domain for Credit Risk modeling.
Stay updated on the latest machine learning advancements, actively identifying and integrating cutting-edge techniques to continuously improve our models and address diverse analytical use cases.
Provide data-driven insights and recommendations that support decision-making processes and enable us to overcome analytical hurdles.
Document your methodologies, experiments, and results in clear and concise terms.
Effectively communicate complex concepts and findings to both technical and non-technical stakeholders.
Requirements
3 - 6 years of experience in credit risk analytics preferably in Banking and Financial Services
Experience with bureau data; feature engineering
Excellent problem-solving and analytical skills, with the ability to work on complex projects and deliver high-quality results.
Proficiency in Python programming languages is must
Experience with large-scale data processing and distributed computing frameworks is a plus.
Strong communication skills, both written and verbal, with the ability to convey complex ideas to diverse stakeholders.
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
Pythonfeature engineeringexploratory data analysis (EDA)machine learningcredit risk modelingdata processingdistributed computing