Department Building

Founded Data Science division and built team of data scientists.

Created system to define, estimate, prioritize, execute, and evaluate success of data team projects.

Established standards for prototypes and production code, incident response policy, and load testing.

Founded Business Intelligence department and built team of analysts and engineers.

Acquired responsibility for Data Engineering department; created and implemented system to define, plan, and prioritize projects. Increased on-time delivery of projects by 10x.

Ongoing Execution

Managed all resource allocation and prioritization across all three data departments.

Oversaw execution on business-critical projects that enabled Terakeet to provide market-differentiating insights to its clients, ranging from patented market analysis and growth projections algorithms to large-scale automated data collection systems.

Collaborated with legal to patent Terakeet’s proprietary technology.

Established corporate position on use of AI and created interdisciplinary AI task group.

Authored and published multiple articles to boost Terakeet’s thought leadership profile in the AI space.

Trained, evaluated, and deployed a variety of models, including a projections model (LSTM), URL classifiers (fine-tuned LLMs), and a content simulation model (quantile-informed stochastic algorithm).

Created departmental standards for model documentation, deployment, and maintenance.

Developed proprietary system of algorithms for growth projections, improving accuracy over additive method by 67%. Projections are used to quantify value to prospects and set KPIs for active clients.

Developed proprietary algorithms for classification, prediction, and projection, including fine-tuned NLP models, multilayer perceptron models, XGBoost models, regression models, and time series models.

Deployed models and other endpoints using Snowflake, Snowpark, OpenAI, HuggingFace, and AWS tooling (SageMaker, Lambda, API Gateway, ECR, S3).

Established a cross-platform roadmap for data science; collaborated with product managers to prioritize.

Led simulation team’s data collection, analysis, and reporting efforts throughout development and implementation cycle for over 10 projects.

Developed machine learning and NLP models for application in interactive learning environments.

Originated A/B testing program to explore product features’ impacts on learning and perceptions.

Developed polished visualizations and dashboards to support client-facing reporting.

Managed team of two employees, overseeing hiring, training, and professional growth.

Led research and data analysis efforts from R&D to stakeholder reporting on over 20 projects.

Created and implemented research protocols to collect data on users’ cognitive processes to inform product improvements during development.

Instated team processes to bring reliability and validity to scoring algorithms for branching adaptive scenarios.

Created internal reporting on simulations’ usage and efficacy using R Shiny, SSRS, and PowerBI.

Developed statistical models to predict student success on Allied Health and nursing certification exams with > 90% accuracy.

Conducted outcomes research to assemble and publish validity evidence for ATI’s flagship assessments in whitepapers and peer-reviewed journals.

Developed scoring QA procedures to ensure reporting accuracy.

Developed R, SQL, and SAS code to leverage machine learning and traditional statistical approaches, including boosted linear and logistic regressions, k-NN, decision trees, random forests, and IRT.