Develop scientific ML Models, processes, to extract knowledge or insights to Perform data analysis to support and develop ML analytical models. Work with ML Solution Architect to define data flow, share data insights, help in feature selection and choose the right ML algorithm for model development

• Perform analysis to support the deployment of ML models across containerized environment
• Improve the quality and actual implementation of computational algorithms and tools
• Good working demonstrable knowledge of supervised and unsupervised ML algorithms.
• Define product requirements for analytics and provide feedback to the product team on ways in which product may be improved.
• Dealing with large amounts of data and extract knowledge or insight to analyze fraud / AML cases obtained from clients and flag off any inconsistencies.
• Proficiency in handling imperfections in data is an important aspect of this role.

Desired Skills:

• Experience in application and knowledge of statistical techniques and concepts (statistical tests, regression, feature selection, time series modeling, etc.) Strong experience with using SQL and EXCEL.
• Strong data analytical skills and well versed with statistical concepts.
• Understanding of database concepts, data integration.
• Strong programming skills in Python version 3.7 and above and ability to rapidly learn new programming tools.
• Experience of building and deploying classification, regression, clustering machine learning models at an enterprise level.
• Well versed with bagging and boosting algorithms such as random Forest, XGBOOST etc.
• Have fair understanding of Logistic regression and various regularization techniques.
• Experience in full AI/ML life cycle from model development, training, deployment, testing, refining and iterating.
• Expert into troubleshooting issues with a variety of production data and past deployed models.