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As an ESG and Sustainability Analyst, I facilitate discussions and present research findings on environmental and social impact assessment, corporate responsibility, and sustainable development initiatives. My role involves translating complex sustainability data into actionable insights for stakeholders, supporting evidence-based decision making, and contributing to impact-driven project strategies through monitoring, evaluation, and sustainability reporting.
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
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Ever stared at a spreadsheet and thought... there's got to be a better way?
That's where I come in. I'm a Power BI specialist who transforms raw financial data into interactive dashboards.
This company had 6 months of unorganized data. No idea which divisions were profitable. Four million dollars stuck in unpaid invoices.
I cleaned the data, built an interactive dashboard, created advanced metrics using DAX, and delivered a full strategic analysis.
Result? They found 1.3 million dollars in working capital they didn't know they had.
That's the power of business intelligence. If you need financial dashboards, data integration, or strategic analysis... I'm your person.
The unpaid invoice problem is one of the most common and most expensive data blind spots in mid-sized businesses — the money exists but nobody can see it clearly enough to act on it. Finding $1.3M in working capital from 6 months of unstructured data is a result that speaks...
Turning environmental data into meaningful intelligence.
A modern web experience exploring how AI and nature can work together.
#webdesign #productdesign #uidesign #uxdesign #climatetech #dashboarddesign #ai #sustainability
Dog Breed Classification System
Built a deep learning computer vision application capable of classifying 120 dog breeds from uploaded images.
Implemented transfer learning using ResNet and EfficientNet architectures with PyTorch and Torchvision.
Improved model performance from ~3% CNN baseline accuracy to 78.97% ensemble accuracy through optimization and fine-tuning.
Applied advanced deep learning techniques including label smoothing, gradual unfreezing, weight decay, learning rate scheduling, and Test Time Augmentation (TTA).
Deployed a production-ready Streamlit application with real-time image inference and probability predictions.
This is interesting! Which dog is the easiest to classify?
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