EXPERIENCE
Limurse
Remote
Jan 2025 — Mar 2025
Built a predictive content engagement model with scikit-learn and XGBoost to forecast optimal posting windows, improving engagement rates by 18% compared to baseline schedules. Developed an NLP-powered recommendation engine using transformer embeddings to cluster high-performing posts and suggest tailored content ideas, reducing creation time for marketing teams by 25%. Implemented an analytics dashboard and RESTful API (FastAPI + Plotly) delivering real-time campaign performance insights and AI-driven suggestions, enabling faster iteration cycles for clients.
Limurse
Remote
Jan 2025 — Mar 2025
Built a predictive content engagement model with scikit-learn and XGBoost to forecast optimal posting windows, improving engagement rates by 18% compared to baseline schedules. Developed an NLP-powered recommendation engine using transformer embeddings to cluster high-performing posts and suggest tailored content ideas, reducing creation time for marketing teams by 25%. Implemented an analytics dashboard and RESTful API (FastAPI + Plotly) delivering real-time campaign performance insights and AI-driven suggestions, enabling faster iteration cycles for clients.
Limurse
Remote
Jan 2025 — Mar 2025
Built a predictive content engagement model with scikit-learn and XGBoost to forecast optimal posting windows, improving engagement rates by 18% compared to baseline schedules. Developed an NLP-powered recommendation engine using transformer embeddings to cluster high-performing posts and suggest tailored content ideas, reducing creation time for marketing teams by 25%. Implemented an analytics dashboard and RESTful API (FastAPI + Plotly) delivering real-time campaign performance insights and AI-driven suggestions, enabling faster iteration cycles for clients.
Swati Web Technologies
Bhopal, MP
Jun 2024 — Aug 2024
Engineered data pipelines using Pandas and NumPy to clean and preprocess historical inventory and sales time-series data. Developed an LSTM-based demand forecasting model in TensorFlow/Keras, reducing forecast error by 15% vs. an ARIMA baseline; used time-series cross-validation and Bayesian hyperparameter tuning to optimize predictive accuracy. Deployed the model as a RESTful API (FastAPI on Docker) and integrated predictions into an internal dashboard (Plotly Dash); improved forecast accuracy by 20%, significantly reducing stockouts and enabling data-driven replenishment decisions.
Swati Web Technologies
Bhopal, MP
Jun 2024 — Aug 2024
Engineered data pipelines using Pandas and NumPy to clean and preprocess historical inventory and sales time-series data. Developed an LSTM-based demand forecasting model in TensorFlow/Keras, reducing forecast error by 15% vs. an ARIMA baseline; used time-series cross-validation and Bayesian hyperparameter tuning to optimize predictive accuracy. Deployed the model as a RESTful API (FastAPI on Docker) and integrated predictions into an internal dashboard (Plotly Dash); improved forecast accuracy by 20%, significantly reducing stockouts and enabling data-driven replenishment decisions.
Swati Web Technologies
Bhopal, MP
Jun 2024 — Aug 2024
Engineered data pipelines using Pandas and NumPy to clean and preprocess historical inventory and sales time-series data. Developed an LSTM-based demand forecasting model in TensorFlow/Keras, reducing forecast error by 15% vs. an ARIMA baseline; used time-series cross-validation and Bayesian hyperparameter tuning to optimize predictive accuracy. Deployed the model as a RESTful API (FastAPI on Docker) and integrated predictions into an internal dashboard (Plotly Dash); improved forecast accuracy by 20%, significantly reducing stockouts and enabling data-driven replenishment decisions.
Looking to start a project or you need consultation? Feel free to contact me.
Hyderabad, TN, India