Experience & Projects

Created an advanced document processing system with support for multiple formats (PDF, CSV, XLSX) and worker threads for asynchronous processing without blocking the main thread.

Developed advanced prompt engineering techniques for LLMs, with specialized templates for each document type, optimized parameter configuration, and advanced response validation.

Implemented an optimized processing pipeline with asynchronous architecture, real-time progress tracking, robust error handling, and memory optimization.

Implemented advanced prompt engineering techniques with a multi-layer system for contextualization, generation, and formatting, significantly improving the relevance and accuracy of responses.

Created a dynamic PDF report generation system from conversations, using Jinja2 for templating and HTML-PDF conversion techniques for structured documents.

Developed a customized user interface in Streamlit with multi-user authentication system, dynamic theming, and accessibility optimizations.

Designed and developed an interactive dashboard with React 18 and Material UI 6 for business KPI visualization, implementing visualizations with Recharts, D3.js, and React Leaflet.

Created a data-driven decision-making solution, implementing KPIs for sales trends, customer segmentation, inventory turnover, and advertising effectiveness through interactive dashboards.

Employed K-means clustering for customer segmentation and created interactive chloropleth maps using GeoJSON data, revealing geographical patterns in sales and customer behavior.

Integrated Google Trends data using time series analysis to anticipate consumer interests, informing inventory management and marketing strategies.

Implemented a multi-stage data processing pipeline, extracting data from SQL databases, storing in MongoDB, processing with pandas and numpy, and integrating external APIs for data enrichment.

Implemented an adaptive forecasting system that selects optimal algorithms based on data characteristics: Prophet for SKUs with sufficient history, SARIMA for products with moderate history, and moving averages as fallback.

Developed a mathematical inventory optimization model incorporating lead time calculations, safety stock determination, capacity constraints, and turnover rate optimization.

Created a modern and responsive dashboard with React/TypeScript, interactive visualizations with Chart.js, component-based architecture, and performance optimization through memoization and lazy loading.

Utilized Storymaps in ArcGIS Online for effective visualization, balancing local and cloud-based processing based on analysis requirements.

Developed gradient-based visualizations using ArcGIS API to highlight competition concentrations across the US, ensuring interpretability through interactive maps.

Integrated AI-driven analysis with geospatial data, validating accuracy with GPT Vision API, to create a comprehensive competitive landscape view.

Created reusable components for clean and maintainable code, including navigation menu, footer, and contact form, which facilitate effective data visualization and user interaction.

Integrated smooth animations using Animate.css library to enhance interactivity and visual appeal, contributing to effective data storytelling and user engagement.

Optimized website performance using Lighthouse, Google PageSpeed Insights, and WebPageTest tools, achieving fast loading times and a fluid user experience with measurable improvements.

Created an intuitive dashboard with filters and KPIs using HTML, CSS, and JavaScript, enabling non-technical stakeholders to select data and view reports easily.

Conducted country-specific analysis of web data, analyzing user behavior patterns and cultural nuances across metrics like Active Users, Engagement Rate, and device preferences, as well as country-specific KPIs such as Build & Price completions and test drive bookings.

Established thorough documentation practices using Google Docs and version control, enhancing project transparency and replicability.

Utilized concurrent processing with ThreadPoolExecutor to efficiently handle multiple SKUs, optimizing computational resources and reducing execution time.

Integrated advanced visualization techniques using Matplotlib and Plotly to create interactive and informative charts for trend analysis and forecast comparison.

Implemented inventory optimization algorithms, including EOQ and safety stock calculations, considering sustainability factors like CO2 emissions.

Leveraged GPU acceleration for deep learning models (LSTM, Transformer) using PyTorch and TensorFlow, significantly improving training speed.

Developed a custom Transformer model architecture for time series forecasting, showcasing adaptability to complex sequential data.

Implemented robust error handling and logging mechanisms to ensure reliable execution across large datasets.

Utilized Optuna for hyperparameter optimization, enhancing model performance through automated tuning.

Integrated geospatial analysis using Folium to visualize geographical patterns in sales and inventory distribution.

Utilized sentiment analysis and topic modeling on open-ended responses to extract key themes and identify market opportunities.

Leveraged Python, pandas, and multi-index for complex data manipulations, creating multi-level pivot tables for cross-sectional analysis.

Optimized code performance using vectorized operations and chunking techniques for efficient large dataset processing.

Generated custom radar and multi-line charts with Plotly, embedded in HTML, enhancing data interpretability.

Balanced company design specifications with data visualization best practices, ensuring accessibility and effective communication.

Applied ANOVA and chi-square tests to identify statistically significant performance issues across groups and sections.

Developed an interactive HTML dashboard for management, featuring dynamic data exploration and highlighting key insights.

Conducted correlation analysis on key economic indicators (GDP, unemployment, inflation, interest rates) versus market trends, implementing lag analysis for optimal impact assessment.

Discovered through rigorous testing that strategies performed poorly, using metrics like Sharpe ratio and maximum drawdown, highlighting the need for advanced techniques in future projects.

Created an interactive Storymaps map in ArcGIS, using templates and pop-ups to present complex geospatial data accessibly.

Performed data cleaning using Python scripts with pandas, addressing inconsistencies and duplicates in large datasets.

Applied pandas and numpy for complex data transformations, including multi-level aggregations and pivot tables for cross-sectional analysis.

Translated analytical findings into strategic recommendations, creating layered dashboards in Looker Studio for different levels of technical understanding.