Arkaprabha Bhattacharyya

Senior Software Engineer

Cognizant Technology Solutions•  April 2020 - Present

1. a. Developed Music Recommendation Service for world's second largest smartphone manufacturer, using music metadata and consumer behaviour. 1. b. Recommendation scenarios include- new songs from favourite artist, songs you may like, top playlist of the genre and playlists consumer may like. 2. Worked as a Data Scientist to implement Time Series and multiple Machine Learning (ML) models for revenue forecasting. 3. Trained and guided mentees on machine learning algorithms and techniques. 4. Technologies: Collaborative Filtering, K- Nearest Neighbor (KNN), Time Series, Python, Hive (Hadoop Ecosystem) 5. Team size: 15

Data Scientist

Infosys Limited•  August 2014 - March 2020

1. Worked as a Data-Scientist to understand the problem statement and provide the best possible model; post exploratory data analysis, data pre-processing and hyper-parameter tuning and optimization. 2. Responsibility: (a) Developed an NLP pipeline that ingests 10-K reports of various publicly traded companies and built a machine learning model to predict the long term stock performance of a company from the financial reports. (b) Web-scrapped 10-K financial reports filed to the United States Securities Exchange Commission (US-SEC), in order to determine financial sentiments of the companies listed on NASDAQ, using Beautiful Soup. (c) Developed a Machine Learning pipeline to predict employee attrition using Random Forest, which helped the organization in planning human intervention to alleviate issues faced by the workers. (d) Data storytelling using Tableau generated dashboards/stories. (e) Deployment of the models at production level using Docker container. 3. Technologies: R, Python 3 (including Pandas, NumPy, Plotly, Beautiful Soup), ML algorithms- Linear regression, Logistic regression, Naive Bayes, Decision Trees, K-Nearest Neighbours, K-Means, Apriori Algorithm, Principal Component Analysis, Time Series analysis, Boosting (XGBoost), Bagging (Random Forest), Text Analytics. 4. Tools: R Studio, Anaconda Navigator, Jupyter Hub (Jupyter Notebook), Microsoft Excel, Eclipse Oxygen IDE, Tableau, Docker. 5. Team size: 4