• Led our Data Science Cost initiative, developed our technical roadmap, designed data architecture, & implemented financial forecasting models that drove quarterly planning across AWS. • Modelled company-wide Employee Cost (>100k employees) with 99% accuracy, enabling the business to develop Financial Targets based on employee attributes (Python, AWS, SQL).• Led a group of scientists/engineers to drive quarterly modelling advancements, and coordinate cross-team initiatives with Finance, Tooling, and Data Engineering, ensuring on-time feature delivery.• Developed a machine learning model to match applicant resumes to job families with 94% accuracy, which was used as a Proof of Concept to enable fungible skills R&D (NLP, PCA, & Decision Tree).• Wrote BRDs, Design Documents, & Scientific Results papers to share complex insights and strategy with non-technical stakeholders, effectively communicating results & recommendations to drive decisions. • Onboarded a team of scientists/engineers and implemented team mechanisms (Agile methodology, templates, & testing requirements), allowing team members to begin contributing within 2 weeks of hire.

• Developed machine learning (ML) model to automate profile matching; multi-tier approach utilized natural language processing (NLP), PCA, Decision Tree classification techniques (Python: scikit-learn, pandas), and AWS Redshift (SQL).• Developed automated talent mapping solution to provide talent insights, which reduced manual research requests from 2-weeks to instantaneous (Python, SQL, QuickSight).• Led scrum development cycles for a team of 7 engineers including sprint planning, standup, retro, & demos; maintained Kanban board and coordinated tickets across team. Developed team Git repository, code review workflow, & lead technical trainings.• Architected and developed cloud ETL for automation of big data ingestion (AWS SageMaker, Glue, Lake Formation, Crawler, Redshift, s3, IAM; PySpark).• Developed multiphase data processing pipeline to automate ad-hoc data ingestion (Docker, Python; AWS Lambda, ECR, CloudWatch, IAM, s3, Redshift, Glue, Athena).• Performed technical development of a multi-phase data mart, including architecture design, database implementation (Redshift), QC, & socialization.

• Developed custom matching algorithm, reducing 3-day manual matching workflow to <10 minutes of automated runtime with user-friendly interface (SQL, Python).• Developed automated post-flagging using text classification and sentiment analysis (scikit-learn), to identify problematic posts and notify response teams.• Developed Snowflake data lake & supporting architecture (Fivetran, AWS) to consolidate MySQL, Pendo, Twilio, and Salesforce data. • Developed dashboards (Tableau, Pendo) to track KPIs such as user engagement, retention, customer experience, user feedback, and content engagement. • Developed automated data pipelines (Python, AWS) utilizing Docker containerization, container management (ECR), and scheduling (Cloudwatch).

• Developed and maintained customized dashboards in PowerBI and Qlik Sense. Performed data preprocessing, analysis, and visualization, and presented results.• Acted as data expert to aid development of internal tools such as Microsoft Teams AI bot and web platform for project management and tracking.• Utilized global, interdepartmental communication to understand customer needs, drive project success, and align output with core company strategies.

• Constructed Sharepoint websites, using Infopath and Sharepoint Designer to develop back-end automated workflows and internal tools.• Created dashboard (Tableau) tracking individual and departmental expenditures, with automated flagging for policy-violations and repeat offenses. • Developed automated workflow to predict future part categorization and shipping rates (using Python, Scikit, Pandas, Jupyter Notebooks).• Developed and optimized SQL queries using Microsoft SQL Server Management Studio.

• Analyzed & visualized data using R, Excel, Spotfire, JMP, and Chromeleon. Wrote technical reports, SOPs, and presentations. • Utilized Python script for data transfer and IronPython for method development.• Coordinated data transfer between departments and created data templates/workflows.

• Analyzed data and control charts using JMP, Chromeleon, Excel, and PowerPoint. • Utilized ELN and SharePoint to save and organize data. • Wrote technical reports and procedures. Presented data and data analysis methods.

• Performed A/B testing and regression analysis on chemical degradation studies.• Analyzed, performed QC, and reported data. Wrote and reviewed SOPs, GLP laboratory logs, and study logbooks.• Acted as coinvestigator for studies performing sample extraction, processing and measurements, and test system preparation. Performed troubleshooting for instruments.• Trained new hires and interns in data analysis and laboratory techniques.