Columbia, Maryland, United States
Dynamic Data Scientist with over 14 years of progressive experience, specializing in developing predictive models, leveraging generative AI, and maintaining data integrity across diverse platforms. Demonstrated leadership in mentoring teams and infusing state-of-the-art AI advancements into corporate strategies. Proficient in enhancing business forecasting accuracy with advanced machine learning techniques and comprehensive trend analysis, facilitating data-driven decision-making. Adept at creating predictive models for areas such as lead conversion, customer retention, and enrollment and providing robust support for regulatory reporting and customer value analysis.
- Led and mentored a team in the development and deployment of predictive models, algorithms, and data integrity across all platforms (AWS, GCP, Azure) - Set up and maintained multi-cloud infrastructure for applied AI and machine learning applications - Developed and deployed a consumer insight application using generative AI, Python, and Streamlit - Led a team to build an AI-based translation app for academic, business, and literature use cases, utilizing advanced LLMs (GPT, Claude, LLaMA) - Led the development of personalized recommendation engine with state-of-the-art Graph Neural Network (GNN) framework - Were continuously integrating the latest advancements in data science and AI into organizational projects
- Transformed forecasting framework with significantly enhanced accuracy in predictions of student enrollment, retention, graduation rates, and call volume - Implemented advanced machine learning techniques for effective business forecasting, marketing, and conversion. Drove major improvements in the organization's data-driven decision-making processes - Crucially enabled informed decision-making among stakeholders through comprehensive analysis of both structured and unstructured data, enhancing interpretation of complex trends for tangible organizational benefits
- Developed a comprehensive lifetime revenue estimation model utilizing individual-level data to drive financial forecasting and strategic planning - Designed and implemented a cohort-based tool for forecasting new student enrollments, enhancing enrollment planning and resource allocation - Created retention risk predictive models for various institutions, significantly contributing to improved student retention strategies - Provided robust support in regulatory reporting and analysis, customer lifetime value analysis, revenue reporting, and campaign analysis, addressing diverse internal client needs - Created different data processing pipelines with stored procedures and task schedulers to automate various reports
- Extracted, merged and cleaned up National Children’s Study research data with SAS - Analyzed and validated relationships across various data sets, found inconsistency and fixed issues with data - Assured data quality to produce final deliverable analysis data
- Modified and interpreted predictive pricing models critical to driving insurance performance with SAS - Performed cluster analysis to encompass demographic patterns in various states and imputed missing values utilizing multiple imputations by SAS - Created test batches and random samples for implementing model changes - Took directions from leadership and executed accordingly to deliver results associated with customer orientation, consistency, and cost structure - Extracted, manipulated, interpreted, analyzed, and communicated data with SAS, Excel VBA, and T-SQL to support and assist business partners focused on both routine and special requests