1. Client interactions in order to understand the problem statement and offer solutions in terms ofproject offers2. Project designing at pre-offer stages in order to present solutions for the Problem statement3. Given a business problem, estimate solution feasibility and potential approaches based on available data 4. Building and Designing the Analytics & Data Science projects from Data extraction, Data understanding, Data Preparation, Model building to model deployment, Model maintenance and model performance monitoring 5. Lead a team (Data Scientist, Data Engineers, MLOps, PowerBI) to deliver Hanes project6. Mentoring to freshers and team7. Part of Interview panel

A seasoned professional dedicated to pushing the boundaries of data science and machine learning todrive operational efficiencies and cost savings across diverse projects. My expertise spans the development of sophisticated models and solutions aimed at revolutionizing processes in areas such asstore prediction, sample size optimization, outlier detection, and process streamlining along with BAU work.

● To predict the area without store- A deep learning classification model leveraging CNN on satellite imagery is being developed to predict the area without store, this resulted in less store visit, saving time & cost from field visits on various census projects.● To predict the store counts - A machine learning multiclass classification model XGboost is under development to classify build-up & non-build up area to provide the store count estimate for budgetary estimate.● To optimize the sample size for field visit - A k-prototype clustering model is being developed to get homogeneous clusters among stores, thus optimizing the sample size for field visits.● To automate outlier detection process - Combination of ADTK python package & RFM model applied to get the potential outlier on monthly sales data.● To fast-track existing processes - Develop the code for data validation & missing value imputation for sales data.● Lead & conducted within team lectures on data cleaning, feature extraction, feature engineering and machine learning models using Python and Microsoft Excel.

Client communication and understanding their need.

Social Media Analytics: Social media analytics is gathering data from social media websites and web scraping newspapers and analyzing that data to make business decisions. It is used to mine customer sentiment in order to support marketing and customer service activities. Developed the platform with word cloud, world map using various data visualization libraries, text mining, NLP and sentiment analysis.Healthcare Analytics: • Data visualization: To facilitate decision making for business development • Cost Optimization: Cost reduction of insurance claim using decision tree.• Shelf life analysis: Determination product shelf life based on stability batch data using ANCOVA.• Disease prediction modelling: Developed prediction model for early detection of coronary heart disease using logistic regression.• Interactive web based application for the developed algorithms using Shiny.

• Responsible for projects related to automobile industry.• Experience in handling large data (in gegabytes).• Development, validation & implementation at client location of RFM model, clustering technique, survival analysis & providing MIS report like top customers, fraud customer, fraud dealer, alert reports as per client requirement.• Discussion with the clients to understand business insights.• Providing training of the developed model at clients location.• Post implementation troubleshooting and maintainance or upgradation of the model.

• Responsible for projects related to gaming industry.• Inspection, cleaning, transforming & performing Data mining using SAS for collected data through the crawler from Google Play. • Developed SAS programs using data mining technique for getting Business trend. Understanding the effect of various variables like price of apps, country wise preferences, size, categories, number of likes, number of downloads and average application ratings. • Shared derived output from data mining to business team for defining marketing strategy.• County wise analysis which concludes that in US & Europe Market Brains & Puzzle Category games are more popular & in South East Asia & China Action type games are more popular.• Used text analysis to determine trending game words (Zombie, run etc)• Price distribution analysis of applications/ games.• Developed a SAS program for getting Revenue on the basis of no. of downloads & MIS report.

• To study various biological processes and apply appropriate statistical techniques for software development as per requirements.• To design an algorithm based on various techniques and work with development team in order to implement the algorithm.• To understand previously implemented algorithms and apply new logic and models in order to improve efficiency of current software.• To implement algorithm temporarily in C programs and make a working model out for its demonstration.• To apply statistical software in algorithm verification for application analysis and testing of final product.• Preparation of various documents as feature list, algorithm, implementation, development queries, testing reports for success and failure percentage of software.