Or Granot

• Developed a machine learning model in Python to optimize media pricing by calculating feature-based multipliers tailored to each client's KPIs - improving cost-per-buy (CPB) efficiency.
• Built and maintained internal dashboards using SQL and Tableau-driven pipelines to track KPIs and performance across multiple departments.
• Delivered high-impact analyses to senior leadership - including the CEO - providing actionable insights that informed strategic and operational decisions.
• Designed a simulation model replicating client decision logic, enhancing media targeting and acquisition strategies.
• Collaborated with DevOps to implement a CI/CD pipeline on Kubernetes for the data team, streamlining deployment, testing, and version control.
• Enhanced data architecture to support scalable, efficient queries across large datasets in AWS environments.

• Led end-to-end data science projects including SQL querying, data preprocessing, modeling, QA, and deployment in AWS environments.
• Designed and ran 7 experiments to detect music fraud, resulting in 3 automated pipelines adopted by the fraud team.
• Built interactive dashboards with Tableau and Redash, streamlining access to complex data and enabling faster decision-making.
• Automated data integration and visualization processes, significantly improving efficiency.
• Collaborated in agile teams to enhance internal fraud detection tools, contributing to a measurable increase in fraud identification accuracy.

• Company Overview: NGO focused on social justice and community development.
• Demonstrated excellent leadership and organizational skills in managing day-to-day operations.
• Developed and oversaw educational programs, ensuring their successful implementation and alignment with organizational goals.
• Set strategic goals, tracked progress, and ensured timely completion of tasks.
• Successfully applied for and secured multiple grants to support various projects and initiatives.
• NGO focused on social justice and community development.

Graduated Summa Cum Laude (GPA: 96.5)

Included in the Dean’s List for outstanding academic performance

Elected Representative for the Faculty of Natural Sciences, Student Council

Hands-on intense six-month training in ML, data pipelines, model evaluation, deep learning, NLP, CV, and data visualization

Includes a 2 month internship in a High Tech Company