Data Scientist

● Analyzed 25+ healthcare databases to identify trends and data issues using Python, Tableau.
● Reduced deployment time by 2 weeks by developing and deploying Generative Adversarial Networks (GANs) and
Diffusion models on AWS SageMaker.
● Collaborated with MLops professionals to deploy GAN models on AWS, improving synthetic data quality by 18%
and generating $25,000 in revenue.

• Analyzed 90+ research papers and journals in 2 months to identify gaps in current synthetic data generation
methods.
• Improved data quality using Diffusion models with binary matrix conversion, ensuring 99.89% clean data.
• Implemented metrics-based model analysis like ROC, F1 scores to evaluate performance and ensure
robustness.
• Collaborated with Data Scientists and Software Engineers bi-weekly to provide updates and align on milestones.

Identified a 15% decline in sell-through rates, impacting profitability and regional sales performance.
● Applied Stochastic Gradient Descent (SGD) to improve forecast accuracy by 22%, enhancing sales strategies.
● Delivered progress reports at the Center of Excellence (COE) monthly, facilitating informed decision-making.
● Designed and deployed CI/CD pipelines for automated machine learning model updates, integrating Jenkins and GitLab CI

• Identified inefficiencies in visualization and tracking processes, addressing a 40% gap in data analysis workflows.
• Deployed 3+ ELK Stack Dashboards for real-time visualization, improving metrics tracking and analysis
efficiency.
• Collaborated in daily scrums with engineers and project managers to streamline operations and clarify objectives