RIYA JAIN

Innovative data scientist with a robust background in machine learning, statistical analysis, and predictive modeling. Skilled in translating complex datasets into actionable insights that drive decision-making and business strategy improvements. Previous work has led to significant enhancements in operational efficiency and revenue growth through data-driven strategies.

• Date of Birth: 08/28/93
• Gender: Female
• Marital Status: Married

Late Mr. Rakesh Jain

• Revenue Forecasting, In this project provide the service to 46 USA and Canada Bank we will analyse the segment of the customers bank and how we generate the direct and indirect revenue forecasting. This will enable the banking company to optimize their retention and acquisition strategy. Implement approach time series, ML models. And also use the technique of recommendation system. Using the visualization tools to get more insights and variability., Data Scientist, Cognizant, 02/23 - Present
• Employee Attrition Forecasting, In this project we forecast the coming 2 years of attrition happening in the company process and subprocess wise so that we cross train the FTE's and implement the strategy in such a way that we develop the plan for volume and transaction part from backend acquire the new business and create the backups., Data Scientist, Cognizant, 02/23 - Present
• E-commerce Analytics, In this project provide the service to Dell Client we will analyze and segment the customers of an ecommerce by using RFM approach. This will enable the e-commerce company to optimize their retention and acquisition strategy. Implement approach K-means cluster. And also use the technique of recommendation system., Software Development Engineer, Calsoft Technologies, 07/22 - 02/23
• Win Prediction, In this project we will analysis and predict the win possibilities of deals/projects for an IT consulting company and see how possibility of winning deal to impact other variables. It will help to manage the effort required to win a deal to meet the growth targets. Implement Logistic Regression and Random Forest., Senior Process Associate, Danfoss Power Solutions, 08/21 - 07/22
• Text Analytics, This is text classification task - sentiment classification. Every document (a line in a data file) is a sentence extracted from social media (blogs). My goal is to classify the sentiment of each sentence into 'positive' or 'negative'. Implement Naïve Bayes theorem., Senior Process Associate, Danfoss Power Solutions, 08/21 - 07/22
• Market Basket Analysis, In this project used Associate rule Mining in supply chain products. Based on Unsupervised Machine learning., Senior Process Associate, Danfoss Power Solutions, 08/21 - 07/22
• Lapse Policy, Build a machine learning model for insurance client predicts if the customer lapse his policy. Logistic and Decision tree were used in prediction., Process Associate, Eaton Technologies, 04/19 - 08/21
• Fraudulent ATM Transaction, Analyze the data to catch up the fraud happen in ATM transaction and find the solution how to prevent it. It is classification problem., Process Associate, Eaton Technologies, 04/19 - 08/21
• Wafer Fault Detection, To build classification methodology to predict the quality wafers sensor based on given training data. Wafers are thin slice semiconductors used in hydraulics part., Process Associate, Eaton Technologies, 04/19 - 08/21
• Analytics Based Lending, Built a Credit Engine which calculates Probability for loan defaulting for SME Customers. This Credit Engine receives data from external/internal API's and processes the data. This data is provided as input to the classification model where probability for defaulting is calculated., Process Associate, Eaton Technologies, 04/19 - 08/21
• Machine Learning Model for Insurance Client, Built a machine learning model for an insurance client which predicts if a customer lapses his policy. Logistic regression and Tree type algorithms were used for prediction., Transit Processing Officer, Mphasis, 07/17 - 04/19
• NLP Techniques for Document Classification, Built a classification model using NLP techniques for predicting document categories for an insurance client. Developed Pre-processing pipeline to clean documents., Transit Processing Officer, Mphasis, 07/17 - 04/19