Department Building

Founded and scaled Data Science and Business Intelligence departments.

Acquired and scaled Data Engineering, increasing on-time delivery of projects by 10x.

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

Created systems for project intake, definition, prioritization, evaluation, and incident response.

Ongoing Execution

Managed resource allocation and prioritization across all three data teams.

Delivered high-impact projects including the Total Addressable Market system, Growth Projections, and Content Recommender, which help quantify clients’ market position and target company efforts.

Developed and patented proprietary technology.

Established corporate position on use of AI and formed oversight group.

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

Trained, evaluated, and deployed models including LSTM Growth Projections, fine-tuned LLM classifiers, and a stochastic content simulation engine.

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

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

Developed classification, prediction, and fine-tuned NLP products using multilayer perceptron, XGBoost, regression, and time series models.

Deployed models and other endpoints using Snowflake, Snowpark, OpenAI, HuggingFace, and AWS.

Led data strategy across 10+ interactive products.

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

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

Led research and data analysis 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.

Developed scoring algorithms for branching adaptive scenarios.

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

Developed predictive models for student outcomes with > 90% accuracy.

Published validity evidence for flagship assessments in peer-reviewed journals and whitepapers.

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.