Software
Analytics
AI / ML
Awardco
Jul 2021 - Present
- Guided the technical and organizational strategy of 5 cross-functional engineering teams delivering analytics, AI/ML, and platform services, partnering with product leaders to define the long-term roadmap and determine resources.
- Managed and developed 4 technical leads, providing technical guidance, performance management, and growth opportunities, with 2 reports growing into group-level roles.
- Established strategy and architecture for a unified data platform, powering analytics and AI/ML use cases across the org, including data ingestion, modeling, and transformation with PySpark, and ML model training, inference, evaluation, and observability with MLFLow.
- Led an AI Task Force, consisting of 12 members across multiple functions, driving company-wide adoption of AI, including policy creation, training, executive strategy alignment, and POCs.
- Hired and led 2 senior ML engineers to deliver the company’s first machine learning initiative, a classification model based on XGBoost, establishing foundational architecture for ML and AI.
- Delivered production-ready systems for generative AI and machine learning capabilities.
- Led and mentored a team of 7 BE, FE, and QA engineers, providing technical guidance and project management, with 8 engineers, collectively, growing into tech lead, architect, or manager roles.
- Built the foundational architecture for analytics tooling, enabling engineering teams to construct fast and scalable queries with complex filtering and powerful visualizations, and reducing implementation time for analytics-based features.
- Created and maintained data pipelines to generate novel insights and reduce dashboard load time.
- Implemented an end-to-end testing framework with Cypress, in use across 16 engineering teams.
- Built the company’s CI/CD strategy for end-to-end tests, increasing release confidence.
- Designed a scalable load testing strategy with Locust, reducing performance regressions.