AYUSH JAMINDAR

Building Machine Learning Algorithms and delivering a final product to customers. Managing and mentoring a team of Data Scientists. Creating impactful analytics presentations. Worked on POC’s to implement Demand Forecasting solutions in Retail and Manufacturing. Implemented and used various ML and Time Series algorithms (Cyclic Boosting, Holt’s winter, Lewandowski, ARIMA). Actively involved in migration of the Demand Forecasting product to Azure environment and deployment using docker.

Building Machine Learning algorithms and strategies for solving Entity Resolution Problem. Devised and implemented Pairwise Matching logic, Transitive closure and Reclustering algorithms. Implemented the above algorithms on spark platform using PySpark. Implemented semi supervised learning to label huge set of unlabeled dataset.

Worked on data from different sources like Facebook, Foursquare, Google, Yelp. Worked on Random Forest Classifier to predict influencer and volume potential using Python. Performed deduplication and linking operations using python dedupe library. Worked on various classifiers like Naïve Bayes, Decision Trees, Random Forest, Support Vector Machine, k-means.

Implemented Loss for Renewal model. Implemented Fraud Analytics models using Python and R. Implemented Broker Tiering model. Worked extensively on NLP using Python and R.