Looks like you have JavaScript disabled. For the full Twine experience, you will need to re-enable it.

I am a data scientist with over a decade of experience in healthcare and telecommunications, specializing in machine learning, analytics, and turning data into strategic value. I thrive on building predictive models, designing forecasting systems, and delivering practical solutions that drive real business results. From Verizon to CVS Health and GE HealthCare, I've consistently translated complex data into actionable insights, led cross-functional collaborations, and advanced production ML with robust governance. My work has reduced churn, improved forecast accuracy, and cut operational costs through scalable tooling and thoughtful experimentation.…I am a data scientist with over a decade of experience in healthcare and telecommunications, specializing in machine learning, analytics, and turning data into strategic value. I thrive on building predictive models, designing forecasting systems, and delivering practical solutions that drive real business results. From Verizon to CVS Health and GE HealthCare, I've consistently translated complex data into actionable insights, led cross-functional collaborations, and advanced production ML with robust governance. My work has reduced churn, improved forecast accuracy, and cut operational costs through scalable tooling and thoughtful experimentation.

David Alden Newberry

Data Scientist, Data Analyst, AI Developer, +2





I am a data scientist with over a decade of experience in healthcare and telecommunications, specializing in machine learning, analytics, and turning data into strategic value. I thrive on building predictive models, designing forecasting systems, and delivering practical solutions that drive real business results. From Verizon to CVS Health and GE HealthCare, I've consistently translated complex data into actionable insights, led cross-functional collaborations, and advanced production ML with robust governance. My work has reduced churn, improved forecast accuracy, and cut operational costs through scalable tooling and thoughtful experimentation.…I am a data scientist with over a decade of experience in healthcare and telecommunications, specializing in machine learning, analytics, and turning data into strategic value. I thrive on building predictive models, designing forecasting systems, and delivering practical solutions that drive real business results. From Verizon to CVS Health and GE HealthCare, I've consistently translated complex data into actionable insights, led cross-functional collaborations, and advanced production ML with robust governance. My work has reduced churn, improved forecast accuracy, and cut operational costs through scalable tooling and thoughtful experimentation.

Available to hire

I am a data scientist with over a decade of experience in healthcare and telecommunications, specializing in machine learning, analytics, and turning data into strategic value. I thrive on building predictive models, designing forecasting systems, and delivering practical solutions that drive real business results.

From Verizon to CVS Health and GE HealthCare, I’ve consistently translated complex data into actionable insights, led cross-functional collaborations, and advanced production ML with robust governance. My work has reduced churn, improved forecast accuracy, and cut operational costs through scalable tooling and thoughtful experimentation.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Work Experience

Data Scientist at Verizon

March 1, 2023 - Present

Drove the creation of a churn-risk model leveraging customer tenure, application usage, and engagement interactions; accelerated model adoption across 3 departments through clear communication of findings. Built network anomaly detection features from time-windowed telemetry and alarms; reduced false positives by 18% while maintaining incident recall for priority events. Integrated a forecasting system to reduce the average amount of overstaffing each quarter, and personally led training for 1 junior data scientist in forecasting. Deployed an internal RAG assistant over troubleshooting docs using embeddings + vector search; increased first-pass issue resolution by 9% and trimmed average handle time by 30 seconds. Hardened model operations with MLflow tracking, drift checks, and automated data quality tests; reduced production alert noise by 35% and cut time-to-diagnose failures from hours to under 30 minutes.

Data Scientist at CVS Health

October 1, 2019 - February 1, 2023

Championed development of risk stratification models for prescription adherence, informing targeted outreach programs that decreased hospital readmission rates among high-risk patients by 3%. Rolled out demand forecasting models for pharmacy inventory, supported by a team of three junior analysts, and improved forecast accuracy by 14% in predicting prescription needs over 3 months. Fashioned SQL-based cohort analyses on 200,000+ patients, revealing key drivers of prescription adherence and informing targeted outreach programs that improved patient outcomes. Launched automated ETL validation processes for claims and member tables, covering schema, null-rate, and outlier rules, which eliminated 2 manual steps, freeing up engineering time each week. Standardized the model deployment process with Docker and Kubernetes, enabling 3 new model deployments and enhancing overall system reliability for real-time predictions.

Data Scientist at GE HealthCare

January 1, 2015 - September 1, 2019

Conceived a comprehensive risk-scoring system for imaging devices using maintenance logs and sensor-derived features; delivered a verified 17% boost in early-warning accuracy. Developed anomaly detection rules and ML baselines for quality signals in imaging workflows; decreased escalations to engineering by 12% through faster triage and clearer root-cause evidence. Accelerated insight generation by designing Python and SQL pipelines that processed over 500,000 equipment records monthly, reducing manual data preparation to under four hours weekly. Pioneered model performance evaluations, sharing key insights with stakeholders and championing the adoption of machine learning models across three sites, improving workflow efficiency by 18%. Introduced experiment tracking and versioned datasets for model iterations; shortened regression investigations from 2 days to same-day resolution during release cycles.