EXPERIENCE

Engineered prediction models for molecular inhibition by leveraging RDKit to generate comprehensive molecular descriptors, significantly enhancing feature selection for downstream modeling tasks

Built a robust machine learning pipeline integrating SVC, Random Forest, and XGBoost models optimized through Optuna, achieving an impressive ROC-AUC of 0.78 with rigorous k-fold cross-validation

Implemented advanced neural architectures including RNN with LSTM and Attention Mechanism components specifically designed for sequential modeling of molecular structures, substantially increasing model sensitivity to subtle molecular properties

Developed and fine-tuned a Directed Message Passing Neural Network (DM-PNN) using Chemprop framework, reaching outstanding performance metrics with an F1 score of 0.87 and PR-AUC of 0.82, directly contributing to enhanced early-stage drug discovery processes

Integrated GPT, Claude, and BioMedLM, a domain-specific large language model, to analyze research literature and extract relevant molecular binding properties, accelerating the identification of promising inhibitor candidates by 40%

Increased website runtime by 35% through strategic file migration to DynamoDB and S3 parallel transfer with optimized indexing, significantly improving user experience for over 50,000 monthly visitors

Led a team of 4 in comprehensive front-end redesign using HTML/CSS and JavaScript, boosting SEO ranking by 15% and ad engagement by 20%, directly contributing to increased revenue

Reduced costs by 50% by evaluating CMS options and communicating integration insights with stakeholders to streamline development processes and operational efficiency

Implemented data analytics pipeline using Google Analytics and custom Python scripts to track user engagement patterns, providing actionable insights that increased article read-through rates by 22%

Developed an automated content recommendation system using collaborative filtering and TF-IDF analysis to personalize article suggestions, increasing user session duration by 40% and reducing bounce rates by 18%

Deployed GPT-3.5 for headline optimization through A/B testing framework, comparing ML-generated headlines against human-written ones, resulting in 27% higher click-through rates for optimized content

Simplified advanced statistical models for non-technical users by converting Python models to Stata functions, significantly improving accessibility and adoption among social science researchers

Achieved 0.95 accuracy for Random Forest with cross-validation through Hat Matrix optimization for n-dimension array predictions, establishing a new departmental benchmark for model performance

Improved Causal Inference methodologies by incorporating Two-Stage Curvature Identification, which elevated algorithm pipeline accuracy by 15% across multiple research datasets

Developed novel time series forecasting techniques combining SARIMA models with feature engineering approaches, reducing prediction error by 23% compared to standard implementations

Created a natural language interface using LangChain and Llama 2, enabling researchers to query statistical results through conversational prompts, reducing analysis time by 65% for non-programmers

Automated the installation and update of 5000+ driver files for Windows Desktops by building a custom Bash script to run on boot, reducing IT support tickets by 73% and saving approximately 120 work hours monthly

Achieved Silhouette Score of 0.89 for clusters of consumer comments, derived from fine-tuned SentenceBERT models in Python, enabling precise market segmentation and targeted campaign development

Elevated score to 0.94 by incorporating Siamese network structures as part of a global effort to streamline marketing using customer feedback, improving product development prioritization

Engineered a time series analysis framework using ARIMA and Prophet to forecast consumer trends from historical feedback data, providing 85% accurate 6-month projections for product planning

Implemented GPT-J fine-tuned on company documentation to automatically generate comprehensive responses to customer support inquiries, reducing response time by 60% while maintaining 92% accuracy

Created a comprehensive internal system for tracking inventory and materials in transport, enhancing order management processes and reducing processing time by 27%

Designed and built a new website from scratch using HTML, CSS, JavaScript, PHP, and SQL to establish the company's digital presence, improving client engagement and accessibility

Developed an automated reporting system using Python and basic ML classification techniques to categorize shipping manifests, reducing manual document processing time by 40%

Implemented database querying and modification tools for efficient inventory access, integrating simple predictive models to anticipate stock requirements based on historical patterns

Established proper documentation for all systems, enabling faster onboarding of new employees and ensuring knowledge transfer across the organization