Omar Tawfik

Delivered end-to-end ML and data engineering solutions across federal and commercial engagements at one of the nation's top defense technology consulting firms, from ETL architecture to production NLP model deployment in cloud-native environments. → Engineered a 10x productivity gain and 50% data accuracy improvement by architecting an end-to-end ETL system in Go, SQL, and Azure, eliminating error-prone manual workflows with automated, auditable pipelines across federal client environments. → Deployed a production NLP fraud detection system using text classification and sequence labeling models in a cloud analytics environment, improving review authenticity detection at scale and presenting model performance and findings to C-suite and technical stakeholders. → Led a 6-person cross-functional team to design and deliver Tableau and Apache Spark analytics dashboards with executive-facing KPIs, driving stakeholder alignment across business and engineering teams for senior leadership consumption. → Scaled platform capacity by 5,000+ monthly active users and improved response time 35% by redesigning Salesforce engagement tracking infrastructure with LTV monitoring and behavioral cohort analysis. → Pioneered a knowledge graph-enriched reporting layer using Looker and optimized BigQuery SQL, strengthening enterprise analytics infrastructure and improving cross-team data accessibility and query performance.

Delivered end-to-end ML and data engineering solutions across federal and commercial engagements at one of the nation's top defense technology consulting firms, from ETL architecture to production NLP model deployment in cloud-native environments. → Engineered a 10x productivity gain and 50% data accuracy improvement by architecting an end-to-end ETL system in Go, SQL, and Azure, eliminating error-prone manual workflows with automated, auditable pipelines across federal client environments. → Deployed a production NLP fraud detection system using text classification and sequence labeling models in a cloud analytics environment, improving review authenticity detection at scale and presenting model performance and findings to C-suite and technical stakeholders. → Led a 6-person cross-functional team to design and deliver Tableau and Apache Spark analytics dashboards with executive-facing KPIs, driving stakeholder alignment across business and engineering teams for senior leadership consumption. → Scaled platform capacity by 5,000+ monthly active users and improved response time 35% by redesigning Salesforce engagement tracking infrastructure with LTV monitoring and behavioral cohort analysis. → Pioneered a knowledge graph-enriched reporting layer using Looker and optimized BigQuery SQL, strengthening enterprise analytics infrastructure and improving cross-team data accessibility and query performance.

Built Python and SQL data pipelines and machine learning models to optimize supply chain operations across a 37-partner distribution network. → Built Python and SQL data pipelines processing logistics, inventory, and carrier performance data across a 37-partner distribution network, replacing manual reporting with automated, scheduled workflows. → Developed demand forecasting models using Prophet and XGBoost that reduced holding costs 35% and improved warehouse utilization across the distribution network. → Improved on-time delivery 28% by leading the Global Flow Optimization Project, integrating real-time logistics data with historical trends to identify and eliminate distribution bottlenecks across 32 e-commerce distributors and 5 wholesale partners. → Applied scikit-learn clustering and classification models for supplier segmentation and anomaly detection, cutting procurement cycle time 15%.

Supported business analytics and operational reporting initiatives as an analyst intern, building dashboards and conducting statistical analyses to inform strategic decisions. ▸ Designed and deployed Tableau and Power BI dashboards providing actionable insights into workforce efficiency and compensation, improving process efficiency by 25%. ▸ Extracted and normalized large datasets from SQL and NoSQL databases, automating workflows and reducing manual processing time by 40%. ▸ Conducted data mining and statistical analyses using Python and SQL to identify trends in employee performance and attrition, influencing $200K+ in strategic business decisions.

Contributed to data analytics and reporting workflows as an analyst intern, using SQL and Python to extract insights and support business decision-making. ▸ Extracted and analyzed large datasets using SQL and Python, producing insights that informed sales strategies and contributed to a 10% increase in sales. ▸ Developed Tableau and Power BI dashboards for inventory management trend visualization, achieving a 15% reduction in holding costs. ▸ Performed regression analysis using Python to improve lead funnels and advertising strategies, influencing a $20,000 revenue boost.

• Launched and scaled a mental health crisis line that supported over 1,200 youth across HCPS and BCPS districts, resulting in a 35% improvement in youth mental health referral outcomes. • Built and deployed a Salesforce-based case management system, enabling real-time tracking of 10,000+ client interactions and improving documentation compliance with national counseling standards by 90%. • Recruited, trained, and managed a team of 25+ licensed counselors and volunteers, expanding service coverage by 300% and cutting average response time from 48 hours to under 12 hours. • Forged partnerships with 9 school districts, 3 local government agencies, and 7 nonprofit organizations, leading to a $150K increase in funding and measurable growth in community impact. • Created data dashboards to track mental health trends, generating actionable insights that influenced school policy decisions affecting over 50,000 students.

BS in Operations Management and Business Analytics with concentration in data-driven decision making, supply chain optimization, and business intelligence. Relevant coursework: Business Statistics, Database Management, Operations Research, and Financial Modeling.

MS in Data Science with focus on machine learning, statistical modeling, natural language processing, and AI systems. Maintaining a 4.0 GPA while working full-time as a Data Scientist at DC Government. Relevant coursework: Machine Learning, Deep Learning, NLP, Big Data Analytics, Statistical Computing, and Causal Inference. Tools and systems: AWS, Azure, GCP, Apache Spark, Airflow, Docker, FastAPI, MLflow, LangChain, HuggingFace, and RAG systems.