Key Responsibilities: • Lead team of 2 Data Science Engineer to build Machine Learning & Deep Learning models for question answering on Open Based dataset. • Prospect Prioritization model based on the past usage of the customers. • Apply Natural Language Processing on client call transcripts to extract key concepts and themes being discussed, identify the intent, key question being asked and map the discussion topics to internal research taxonomy using Machine Learning Models. • Use Machine Learning to develop self-correcting algorithms to cluster topics being discussed in calls to draw meaningful insights. • NLP(BERT and ELMO) based tagging of Question of Clients to SME’s. • Articulating insights, models, mathematical and statistical concepts to non-technical stakeholders. Skills/Domain: Machine Learning, Deep Learning, PyTorch, R-CNN Dev & Prod Environment: GPUs, AWS

Key Responsibilities: • Working with the team of Data Science Engineer to build end to end Machine Learning platform. • Design proofs of concepts (POC) to answer targeted business questions. • Building Machine Learning & Deep Learning models using Adobe clickstream data. • Creating spark-based workflows for Data processing and feature Engineering. • Scaling the solutions for all 35 markets (different geos). • Articulating insights, models, mathematical and statistical concepts to non-technical stakeholders. • Building Recommendation Engine based on the Item-Item similarity. • Predicting the customers who are likely to be converted using Random Forest and XGBoost. • Preparing data flow pipeline in python for processing and feature Engineering. • Used python's Scikit learn for building classification model. • Running campaign’s to reach out to potential customers. Skills/Domain: Machine Learning, Deep Learning, Python, Apache Spark, Databricks Dev & Prod Environment: Microsoft Azure platform, HDInsight, DWH, Datalake

Key Responsibilities: • Building the PySpark based pipeline for data processing and wrote Hive Queries to fetch the data from CornerStone. • Worked on K-Means clustering algorithms to detect and avoid fraud transactions. • Developed the Python code in Spark environment to produce the desired results for the customers, using which customers make business decisions. • Experienced with the Spark improving the performance and optimization of the algorithms in Hadoop using Spark Context, Spark-SQL, Data Frame, and Pair RDD's. • Responsible for design & development of Spark SQL Scripts using Python. Skills/Domain: Machine Learning, Python, Apache Spark Dev & Prod Environment: MAPR, CornerStone, CRON