Experience with machine learning algorithms such as logistic/linear, Random Forest, Xgboost required
Role overview
Responsibilities
Own end to end business problems and metrics, build and implement ML solutions
Design, experiment and evaluate innovative models for predictive learning
Establish scalable, efficient, and automated process for large scale data analysis, feature creation, model development and deployment
Ability to identify key drivers of business and create KPIs/modeling solutions for the same
Define, design and deliver solutions using data science/analytics in fast paced environment
Evaluate and apply machine learning algorithms to build variety of data science models particularly in credit risk/unsecured lending domain but not limited to
Complete ownership including but not limited to identifying model development approach, building ML models, evaluation, cost benefit analysis, exploration of new data sources, implementation and monitoring of developed models.
Working closely with engineering team for deployment of models and infrastructure development.
Requirements
Experience: 2-6 Years
Exposure/Experience in developing Machine Learning models using various algorithms (logistic/linear, Random Forest. Xgboost ,etc)
Strong background in business analysis (consumer/business strategy, financial products, pricing, etc.) with very strong data analysis (Sql, excel, etc,) experience
Strong understanding of how to structure analysis and to solve real world business problems.
Hands on experience in R or Python is must
Nice to have
Data Scientist preferably from financial services, large banks/MNCs.
Expertise in end-to-end model development and model lifecycle management (develop, deploy, monitor) is preferred
Good business understanding of fintech/personal lending space is preferred
Zobhira
