Chandler Ault

VP of Engineering at Medvelle (2025-10 – Present)

Led engineering team managing data integration pipelines, AI-driven procurement systems, and event-driven backend infrastructure handling $1M+ in weekly transaction volume.

• Developed and managed data integration pipelines connecting client ERP and financial systems (NetSuite, Sage, QuickBooks) with internal data platforms.
• Built an AI-driven procurement agent network that automated end-to-end procurement workflows, reducing manual processing time by ~25 minutes per order and cutting operating costs by ~30%.
• Designed event-driven backend infrastructure handling $1M+ in weekly transaction volume, emphasizing reliability, idempotency, and observability across distributed services.
• Led cross-functional engineering efforts across operations, finance, and support teams, translating process requirements and feature requests into clean, maintainable software.
• Served as the final technical approver for production releases, enforcing quality, performance, and system integrity standards.
• Helped establish coding standards and pull request (PR) protocols.

Software Engineer at Medvelle (2024-06 – 2025-10)

Engineered financial systems including event ingestion, dashboards, invoicing automation, and data reconciliation tools.

• Engineered a fault-tolerant financial event ingestion system (Flask/Python) processing thousands of records daily with retry logic, idempotency guarantees, and sub-second latency.
• Built a financial dashboard with customizable reporting and live data visualizations, enabling clients to monitor financial position in real time.
• Automated invoicing workflows, cutting processing time from 8 hours to under 1 hour.
• Optimized SQL queries and indexing strategies to cut page load times from 60+ seconds to under 5 seconds, and developed a data reconciliation algorithm that recovered $30K+ in previously undetected discrepancies.
• Built internal support platform for handling requests from 1,000+ users, improving issue tracking and resolution speed.
• Initiated and led the development of a vendor data platform providing sales representatives with real-time insights to improve client coordination and optimize recommendations and promotional targeting.

Data Scientist at XPN (2023-06 – 2024-06)

Built data pipelines, ML models, and dashboards for advertising performance analysis and customer lifetime value prediction.

• Built data pipelines and KPI dashboards that transformed raw advertisement data into performance metrics.
• Trained an ensemble model to predict customer lifetime value, enabling clients to assess the long-term profitability of their Amazon DSP campaigns.
• Optimized BigQuery SQL pipelines processing 1TB+ of daily data, reducing compute costs by 15% through query restructuring, partitioning, and schema improvements.
• Built interactive Plotly visualizations to communicate model outputs and campaign performance to non-technical stakeholders.

M.S. in Data Science – University of Washington (2025)

B.S. in Computer Science – Princeton University