Managing projects in Machine Learning, including MMX and test-market control.Leading Deep Learning projects, such as developing a sequential model pipeline for live pharmaceutical data.Overseeing GENAi initiatives, focusing on Primary Intelligence surveys, Question-Answer generation, and more.Led the Product Development lifecycle for the complete product launch, including sales force incentive design and optimization, and measurement of effectiveness

Organization is a sister company of Convergytics.

𝐌𝐚𝐫𝐤𝐞𝐭 𝐌𝐢𝐱 𝐌𝐨𝐝𝐞𝐥𝐥𝐢𝐧𝐠:Injected the Market Mix Model into the pipeline for the Pharma Company, marking the effectiveness of marketing strategy and ROIs for each Media Channels Lead a Team in building a Budget Allocation-Optimization Simulation tool. Helped in Planning Strategies for the Spend Allocation hence improving the Overall ROI by 3.8%.𝐂𝐚𝐦𝐩𝐚𝐢𝐠𝐧 𝐄𝐟𝐟𝐞𝐜𝐭𝐢𝐯𝐞𝐧𝐞𝐬𝐬 𝐌𝐞𝐚𝐬𝐮𝐫𝐞𝐦𝐞𝐧𝐭:Developed methodology for control-market selection fulfilling the primary motive of measuring the effectiveness of previously executed campaign programs.𝐃𝐄𝐋𝐋 -𝐄𝐌𝐂 - 𝐂𝐄𝐑𝐍Helped in Developing a Data Cleansing and Enrichment pipeline for 500k data points. Processing, Validating the Multilingual Geolocation data. Flagging them as Commercial, Residential, or Data Centre based on the web data, using NLP methodology i.e., NER, Entity Resolution, Address Parsing, and Multilingual Translation.Developed a Sample Knowledge graph for the distribution network marking the Address type as an entity.Learned to work in a Production Environment𝐌𝐞𝐭𝐡𝐨𝐝𝐨𝐥𝐨𝐠𝐲Models : Mixed LM, Linear Regressions, Logistic Regressions Polynomial Regression, Non-Linear Modelling, Bayesian Model, SVC, KNN, Propensity Scoring, DBSCAN, GLM, Decisions Trees, Ensemble Models, Sentiment Analysis, Predictive Analysis, Markov Chain, David Hill Equation, Hills Equation, Gekko, SciPy Optimization, SaSLm, Matching Algorithm, NLP, SciPy, NER, Tiger Graph API : Lib Postal (Address Parsing), Translations, cld1, cld2( Unicode data Annotation). Documentations : L1, L2 Flow Diagrams, Process Documentation, Language : Python, R, SQL, C++

Enterprise Search Engine -Knowledge Graph - Headed a team of 4 for developing Knowledge graph,which will attribute to the search result.- Leverages the Knowledge graph generated using Cosmos-DB ,using germlin API. - Worked on the code optimization for same , reduced the searched time by 26% that of initial run.Price Prediction -Regression- Feature Engineering of the independent features, by removing the correlation among the features,and making them apt for the model input- Generation of Embedding for the unique keywords for the features having textual behaviour-

Multilevel Classification - Released a Poc,by solving a multilevel classification problem based on the user's mail intent ,for the financial domain client. Worked on Data Cleaning, Annotation, Feature Engineering,Model creation, Deployment .Text Segmentation- Build complete end to end Text Segmentation model ,where sentences are segmented based on the activity mentioned in the sentence. Idea was to develop a baseline Deep learning Model.

This was for the shake of semester marks as it was compulsory to showcase at least one internship letter to the college. Learned some professional ethics , alteast got chance to experience Offline office culture .Also Learned Bunking Office is not a good idea.🙂