Data driven professional with around 3+ years of experience which include.Projects in data analysis, data visualization, data cleaning, data modelling, machine learning, LLM, RAG, Langchain and also completed the executi…
Data driven professional with around 3+ years of experience which include.Projects in data analysis, data visualization, data cleaning, data modelling, machine learning, LLM, RAG, Langchain and also completed the executive post graduate program in data science Proficient in data mining and data visualization to deliver compelling business value to clients & successfully execute projects. Based on my experience I am looking for data analytics where I can use my skills and experiences
Agentic AI 1. Automated category & brand analysis using Agentic AI (LangChain + OpenAI). 2. Reduced manual work from 1 day to 3 hours. 3. Delivered subcategory analysis, correlations, model groups & brand overlaps. 4. Exported a final Excel report with insights into decision-making. Automation 1. Automated the exclude flag analysis process in Python. 2. Applied client-defined conditions to exclude irrelevant brands. 3. Excluded Walmart private brands from the analysis. 4. Reduced manual effort from 2 days to 4–5 hours. Skills: `SQL, EDA, Feature Engineering, Ridge Regression, Bayesian Modeling, ROI Analysis, Media Spend Optimization, Tableau, Generative AI, Agentic AI, Fuzzy Matching, DuckDuckGo API, Python
As a Data Scientist, I leverage advanced analytical techniques to extract meaningful insights from complex datasets. Responsibilities include data preprocessing, feature engineering, and model development. Proficient in Python, SQL, and machine learning algorithms, NLP, FuzzyWuzzy, Jaccard, Deep Learning, Tensorflow, Neural Network Generative AI, LLM, Langchain. Expertise in data visualization tools like Tableau for clear and impactful presentation of findings. Collaborate with cross-functional teams to drive data-driven decision-making and optimize business processes
Results-driven professional with expertise in marketing analytics and product insights. Skilled in assessing the effectiveness of pharmaceutical marketing campaigns, tracking customer retention and acquisition, and optimizing marketing strategies. Proficient in analyzing user behavior, conducting user segmentation, and collaborating with cross-functional teams to enhance product performance and user experience. Experienced in developing and implementing data transformations to improve data quality, streamline analytics workflows, and optimize query performance. Demonstrated ability to support data migration and create automated solutions, such as bots, for delivering real-time reports to leadership. Tech Stack-SQL
Leveraged OpenAI Generative AI to create synthetic datasets for testing, simulation, and automation, especially where real data was limited or unavailable. Developed an agentic AI tool that integrated: A live web search tool (DuckDuckGo) to enrich brand and trend data. A fuzzy matching tool to identify and group similar brand names. Enabled the agent to automatically cluster similar brands, reducing manual data cleaning and reconciliation efforts. Extended the agentic AI to analyze time-series charts, detecting peaks and associating them with relevant holidays or events using contextual reasoning. Resulted in significant time savings and enhanced accuracy in both brand consolidation and trend attribution processes.
Steps Involved Load PDF using document loader. Splitting Text into Each question called chunks. Storing chunks into a Vector Database in the form of Embeddings. Creating a retrieval Question-Answer chain by retrieving the context from the vector database Creating a conversation retrieval Question-Answer chain using chat history
Led the integration and synchronization of GP data across 16 hospitals and associated labs under IHH Malaysia, unifying patient records for enhanced care delivery. The project utilized Hospital Information Systems (HIS) and Laboratory Information Systems (LIS), along with manual tracking (MT) data, to address fragmented records, improve communication, and reduce redundancies. Key achievements include implementing advanced data matching algorithms like FuzzyWuzzy and Jaccard Similarity, improving data accuracy by 25%. A UID generation pipeline was developed using Azure Storage, ensuring precise GP ID validation through NLP and LSTM-based neural networks. This system prevented duplicate UID creation and ensured data integrity. Additionally, a unified data repository was created to streamline operations, boost marketing and care delivery strategies, and enhance commercial efficiency by 30%. The project leveraged a robust tech stack, including Python, Azure Data Factory, NLP, and advance
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