• Designed and productionized ML-driven anomaly detection pipelines on high-frequency telemetry data, integrating dbt + Databricks + React to create customer-facing alerts with real-time latency guarantees to proactively mitigate >98% of incidents.

• Delivered critical product safety insights using dbt, bootstrap sampling, and Poisson distribution analysis. Reported findings via white paper and Tableau dashboard, informing org-wide safety strategy.

• Re-architected data pipeline with dbt, cutting ETL costs by $250,000 and reducing development time by 10x.

• Introduced CI/CD automation and Pythonic standards, improving code quality and trust in data deliverables.

• Led analytics infrastructure, mentoring stakeholders on data pipelines, improving self-service, and increasing team bandwidth by 30%.

• Defined and surfaced KPIs in dashboards, improving CS strategy and increasing CSAT scores by 5%.

• Averted ~$1.5M battery recall through statistical analysis and de-risking, leading to new detection features.

• Developed a Python-based analytics platform to analyze telemetry data, reducing RCA time by 50%.

• Developed Python-based image and signal processing app, securing $13M from NIH RADx.

• Improved assay accuracy by 2% through statistical analysis (linear regression).

• Built a data warehouse of materials and manufactured goods inventory, establishing an end-to-end lineage of kitted products.

• Developed Jupyter web apps (Python, Plotly, Matplotlib) for kit validation using ROC curves, Monte Carlo simulations, and box plots, enabling the release of 300,000+ COVID-19 tests.

• Developed automated data analysis workflows (Matplotlib, Seaborn, Plotly) on a Jupyter server, reducing data processing time from 45 minutes to sub-minute, expediting COVID and Flu R&D.

• Captured and analyzed data for the US-IVD NIPT launch for Harmony Pre-natal Assay