Bachelor's or higher degree in a quantitative field such as Statistics, Mathematics, or Data Science.
At least 3 years of professional experience in quantitative analytics, including projects with regression, Bayesian methods, and clustering.
Minimum 1 year of experience in Credit Risk analysis and machine learning techniques like random forest and neural networks.
Proficiency in Python and SQL for data mining and modeling tasks.
Requirements:
Perform data quality assessments and market share analyses.
Conduct complex statistical analyses under supervision.
Integrate and assess data from multiple platforms for analytical purposes.
Communicate complex analytical findings clearly and develop innovative solutions.
Job description
Perform necessary analysis, such as data quality assessment, recommendation of Generic Score which predicts customer’s portfolio, Market share analysis, competitor analysis etc. ·
Work extensively on various types of data like Credit, telecom and utility, demographic and other alternative data ·
Conduct complex statistical analysis under the supervision of Manager ·
Complex data assessment and integration from multiple platforms as required for analytical purposes
Desired traits •
3+ years professional level quantitative analytical experience, including conducting handson analytics projects using generalized regression models, Bayesian methods, clustering, and similar methodologies
At least 1 year of professional level quantitative analytical experience in Credit Risk, machine learning using random forest, gradient boosting, neural networks, support vector machine, and similar methodologies
Proficiency skill in handson data mining and modeling projects with Python and SQL.
Strong consultative acumen and ability to understand complex analytical solutions
Ability to communicate complex quantitative analysis in a clear, precise, and actionable manner
Ability to create new ideas for analytical solutions to address customers business issues
Apt with MS Office components (Word, Excel, Access, PowerPoint) and Google G Suite Preferred Experience
Experience in cloudbased data engineering andor machine learning with Google Cloud Platform, AWS, or Azure
Experience working with big data distributed computing tools (Hadoop Streaming, MapReduce, Spark)
Exposure to Visualization tools such as Spotfire a plus
Extensive knowledge in Credit Risk Models
Strong knowledge of credit bureau data is plus Technical Skills – Desirable
Basic knowledge of SAS
Strong knowledge of Python and SQL
Candidates with Google Cloud based machine learning, Spotfire and data engineering experience will get additional weightage
