With a cross-functional background in data science, business strategy, and marketing analytics, I bring a unique blend of technical and business acumen. I’ve led impactful projects in telecom, healthcare, and e-commerce—driving growth, optimizing processes, and enabling data-driven decisions using tools like Python, Tableau, and machine learning models.
To leverage advanced analytics and generative AI to solve complex problems, improve human experiences, and enable smarter business decisions.
To utilize cutting-edge data science techniques to solve complex problems and create impactful solutions.
Translating raw data into clear insights using Tableau, Streamlit, and Python-based tools; delivering dashboards and visualizations to guide business strategy.
Developed classification, regression, clustering, and forecasting models using techniques like XGBoost, PCA, Logistic Regression, and Neural Networks.
Forecasted churn, conversion, and sales performance using statistical modeling and time-series methods such as ARIMA and panel regression.
Integrated LLMs, Langchain, and RAG pipelines to build chatbot solutions and AI-driven applications, including prompt engineering and fine-tuning.
Prepared large-scale datasets through transformation, outlier handling, and feature engineering to enhance model accuracy and insight quality.
Created interactive dashboards using Streamlit and Tableau to visualize KPIs, marketing funnels, user journeys, and real-time business metrics.
Used regression and A/B testing to increase ad conversion by 20% and reduce CPA by 30%. Created Tableau dashboards for user journey and campaign performance.
Processed and analyzed over 10,000 YouTube comments using Python (NLTK, BeautifulSoup, TextBlob). Built an interactive Streamlit app to boost sentiment analysis speed by 30%.
Built a high-accuracy churn prediction model using Logistic Regression and Random Forest with hyperparameter tuning. Deployed it via Streamlit for real-time analysis, increasing stakeholder adoption by 40%.