Caleb Dame

Lead development of predictive models for strategic product marketing, churn prediction, and collections optimization used by credit unions and utility partners, directly impacting program adoption rates and retention strategies.

Drive to market a privacy-affirming strategy of serving ensemble federated prediction models, aggregating insights to develop generic, industry-wide product models while protecting client data privacy.

Building scalable internal tooling to standardize data science processes, streamline project throughput, monitor model performance, and expose model interpretability to end users.

Built ML-driven lead qualification models that improved sales efficiency by eliminating 20–40% of low-quality meetings and increased top salesperson conversion rates 2–3x.

Designed and managed ongoing A/B testing programs to optimize marketing funnel performance and application outcomes.

Created and oversaw CRM integrations (HubSpot, Close, Zapier) and real-time dashboards to enable data-driven marketing decisions.

Developed fraud detection models using LightGBM and XGBoost, and maintained Python packages for measuring drift in unlabeled production data.

Implemented advanced ML architectures (Autoencoders, Stacked Models, and Graph Neural Networks) to research applications for identifying fraud networks.

Led cross-team collaborations, integrating newly acquired data assets to enrich features and improve model performance.

Designed and maintained 15+ ETL pipelines in Databricks, Airflow, and Snowflake, processing 3.5 TB of transformed data daily for financial analysts.

Built automated anomaly detection using clustering algorithms, increasing data purity by 10–15% and enabling real-time visualization in Tableau dashboards.

Researched ML applications (XGBoost, Neural Networks, Naive Bayes) to extract insights from large-scale census data.

Managed large-scale databases in MS SQL Server, ultimately reducing record-linking error by 80%.