Professional Experience

• Built automated data pipelines using Python, AWS S3, and Microsoft Fabric to connect engineering and vendor data, improving tracking, reporting, and dashboard visibility
• Developed Python automation tools that eliminated hours of repetitive manual work by automating data validation and routine engineering processes
• Created automated workflows that reduced manual processing by roughly 30 minutes per team each day, freeing teams for higher-value work
• Collaborate with engineering, vendors, and cross-functional teams to improve data quality, streamline reporting, and support project execution

• Selected as an original member of Apple's AI/ML data collection initiative, helping establish the operational standards, workflows, and protocols adopted across future moderator programs
• Led end-to-end project planning with engineering, legal, and cross-functional stakeholders to define requirements, timelines, compliance processes, and scalable execution strategies
• Successfully led the technical development and operational rollout of 9+ AI/ML data collection projects in approximately 18 months
• Architected repeatable, scalable collection frameworks that let projects transition seamlessly to external vendors after internal validation
• Managed daily communication with engineering teams and stakeholders to keep milestones, collection quotas, and quality objectives on track

• Developed and deployed Python automation tools using Linux, SSH, and internal systems to extract data from prototype Apple devices, reducing manual effort and improving collection efficiency
• Designed and implemented a scalable data ingestion pipeline for processing and uploading large-scale datasets into internal infrastructure
• Built internal metadata management tools and user interfaces that streamlined annotation workflows while improving consistency and data quality
• Engineered automated validation and quality assurance checks verifying metadata integrity before ingestion, significantly reducing downstream errors
• Contributed to an internal application enabling synchronized multi-device recording, improving efficiency during large-scale AI data collection
• Rapidly developed numerous internal automation tools using Python and AI-assisted development, accelerating engineering workflows

• Helped develop an internal GIS routing application using Apple Maps APIs to improve route planning and field data collection
• Built routing solutions accounting for traffic, construction, road closures, and map updates to improve operational efficiency
• Developed geospatial data workflows that adapted to changing routing requirements and improved field planning

• Installed and configured experimental operating system builds on prototype hardware, preparing devices for engineering validation and production data collection
• Created dashboards and reporting tools to monitor collection progress, project health, throughput, and quality metrics
• Trained over 40 Apple moderators, 2 project managers, and 3 backend/data engineers on collection procedures, tooling, prototype devices, and operational best practices
• Performed quality assurance on collected assets, maintaining strict standards before delivery to engineering teams

• Developed forecasting models using Python and Excel, reducing shipping costs by 30% through inventory optimization
• Built automated dashboards to monitor revenue, inventory, and operational KPIs
• Launched and scaled a direct-to-consumer e-commerce store with Shopify and Amazon integrations
• Analyzed customer behavior and sales data to inform pricing, inventory, promotions, and logistics decisions
• Managed e-commerce integrations and automated analytics workflows for operations and SEO optimization
• Achieved full tax compliance and legal registration while minimizing administrative overhead

• Built Tableau/Python dashboards and predictive ML models
• Developed bike-sharing predictor with 99.998% accuracy
• Created Kaggle-winning survival model
• Analyzed U.S. demographic and salary data using Python and SQL to uncover location-based trends
• Collaborated with Data Mining and ML teams on 9 cross-functional projects

• Built a transportation demand forecasting model using Python to predict bicycle demand across multiple locations
• Applied GIS and geospatial analysis to optimize bicycle distribution and routing decisions
• Combined weather, historical ridership, and geographic data to improve forecasting accuracy and operational planning
• Led a 7-member team to develop a random forest model predicting daily bike demand (99.9% accuracy)
• Achieved top 10% ranking in the Kaggle Titanic Competition (700 participants)
• Built an insurance eligibility classifier using logistic regression and a housing price estimator using random forest
• Created a Honda car sales predictor using linear regression with time series, and a custom Ataxx game AI

• Applied refinery safety protocols, hazard and operability studies (HAZOP), and incident reporting procedures
• Practiced maintenance planning, PPE use, and effective responses to unplanned equipment failures
• Trained in leadership techniques, chemical health hazard mapping, and emergency procedures
• Gained hands-on exposure to valves, pumps, compressors, furnaces, heat exchangers, and steam systems
• Earned ROP Certification in Refinery/Process Plant Operations