Data & Machine Learning Consultant Machine Learning Engineer Data

Samia Saad, PMP

WAYNE, NJ ***** 347-***-**** *****.*****@*******.*** US Citizen [LinkedIn SamiaSaad11 (Samia Saad)

Data & Machine Learning Consultant Data Engineering and Analytics Expert PMP Certified

Data and Machine Learning consultant with 7+ years of experience combining technical depth with strategic thinking. Known for asking “why” before “how,” and proactively identifying whether solutions are worth building. Quickly self-learn new technologies to solve high-impact business problems and bridge technical and stakeholder priorities.

Expert in Python, R, SQL, and cloud platforms (AWS, GCP), with advanced certifications including PMP and Harvard Business School Business Analytics. Skilled in stakeholder management, data visualization (Tableau, Power BI), and building data-driven strategies that optimize ROI and operational performance.

Security Clearance: Public Trust Cleared

Technical Skills

Programming & Query Languages: Python, R, SQL,BigQuery

Machine Learning & AI: Scikit-learn, TensorFlow, PyTorch, Vertex AI, Feature Engineering, Predictive Modeling

Data Engineering & Big Data: ETL Pipelines, Spark, Data Wrangling, Data Quality Tools

Cloud Platforms: AWS (S3, EC2, Redshift), Google Cloud (Big Query), Cloud Data Warehousing

Data Visualization & BI: Tableau, Power BI, Matplotlib, Seaborn

Databases: MySQL, PostgreSQL, MS SQL Server

Project & Workflow Tools: ServiceNow, Excel (PivotTables, VLOOKUP), Agile/Scrum

Key Career Highlights

IRS Audit Risk Modeling & Automation using Vertex AI and Big Query reduced query costs and improved performance by 40%.

Developed ML models achieve 91% classification accuracy, boosting marketing campaign ROI by 20%.

Designed ETL pipelines and cloud-based data warehouses that reduced data processing time by 30%.

Led cross-functional teams of 10+ developers to deliver high-impact data projects on time and under budget.

Designed and deployed predictive models to forecast customer behavior, increasing marketing ROI by 22%.at Deloitte.

Professional Experience

Data & Machine Learning Consultant at Internal Revenue Services (Contract) July 2023 – Present

Improved audit precision by ~23%, reducing false positives and reallocating audit capacity more efficiently.

Proactively challenged early model design to eliminate low-signal features, focusing on interpretable and actionable outputs to improve stakeholder adoption.

Worked directly with compliance officers and IRS analysts to translate audit domain logic into model features and decision thresholds. Facilitated workshops to explain model insights, gain trust, and support responsible AI adoption.

Collaborated with data scientists and software developers to build and maintain end-to-end data solutions, ensuring reliable input pipelines for analytics and ML models.

Self-taught advanced Google Cloud tools (Vertex AI, BigQuery, Pipelines) to build and optimize scalable ML workflows with minimal engineering support

Tools & Tech: Google Cloud Platform (Big Query, Vertex AI Pipelines, AutoML Tables, Model Monitoring), Python, SQL, Pandas, PyTorch

Senior Data & Strategy Consultant at Deloitte Consulting March 2022- June 2023

Designed and deployed predictive models to forecast customer behavior, increasing marketing ROI by 22%.

Built a time series forecasting tool for marketing and ops teams using XGBoost with engineered features (lagged values, rolling stats, date encodings). Data was pulled from BigQuery, and forecasts visualized through Streamlit

Empowered non-technical users to run scenario forecasts, leading to quicker planning cycles for marketing and ops teams.

Led agile, cross-functional teams delivering advanced analytics solutions aligned with strategic business goals.

Influenced business decision-making by shaping model outputs and thresholds to reflect real-world risk tolerances and operational constraints.

Tools & Tech: Python, Pandas, Prophet (Time Series), Streamlit, BigQuery

Data Engineer at Drew Marine January 2018- March 2021

Built scalable ETL pipelines integrating multiple data sources for real-time business intelligence.

Developed cloud-based data warehouse infrastructure on AWS, reducing data retrieval time by 40%.

Automated data quality checks and implemented data monitoring tools that improved reporting accuracy and partnered with data scientists to ensure data accessibility for analytics and ML models.

Tools & Tech: Python, SQL, AWS (S3, Redshift, EC2, Lambda), Tableau, Spark, Data Quality Frameworks, Git

Project Highlights

IRS Audit Risk Modeling & Automation using Vertex AI and Big Query (2024 – Present)

Led the development of a predictive model to identify high-risk taxpayer filings, automating audit risk scoring using historical audit data and structured metadata enhancing audit precision and reducing manual screening hours.

Engineered 30+ behavioral and financial features and benchmarked classification models using Vertex AI AutoML Tables and custom Python pipelines to optimize detection of high-risk cases.

Designed and optimized Big Query-based ETL pipelines processing millions of IRS records; implemented partitioning and clustering strategies to reduce query costs and improve performance by 40%.

Integrated the solution into Vertex AI Pipelines, enabling scalable, reproducible model training and batch scoring workflows with scheduled retraining and monitoring via Cloud Functions and Vertex Model Monitoring.

AI-Powered Forecasting Tool using XGBoost & BigQuery (2022)

Developed a time series forecasting solution using XGBoost with engineered features (lagged values, rolling stats, date encodings), enabling accurate short-term predictions for marketing and ops teams.

Integrated BigQuery and Streamlit to build an interactive forecasting tool that outperformed Prophet on benchmark KPIs and supported non-technical users through a streamlined UI.

Reduced planning cycle time by 30% by enabling teams to run scenario forecasts and respond faster to shifts in traffic and sales trends.

Incorporated stakeholder feedback into model output design, aligning forecasts with business planning needs and boosting cross-functional trust in AI-driven insights.

Bank Marketing Analysis (2025)

Developed ML models using Python (Pandas, NumPy, Scikit-learn) to predict client subscription rates.

Achieved 91.2% accuracy with Random Forest Classifier, enabling targeted marketing and improved ROI.

Visualized key insights with Matplotlib and Seaborn to support executive decision-making.

Additional Early Career Experience

Data Quality Intern Critical Mention, Business Data Analyst SILKBANK, Data Analyst KALSOFT

Education/Certifications

Data Science Certification, Springboard, 2025

Scrum Master Certified, 2023

Google Cloud Professional Certified, 2022

Project Management Professional (PMP), 2022

Harvard Business School, Executive Education in Business Analytics, 2021

Master of Science, Computer Science, Montclair State University, GPA 3.8, 2019

Bachelor of Science, Software Engineering, Bahria University

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