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Rotem Margalit — Data Scientist
Data Scientist at Paycor
Production ML · Multi-Agent Systems · A2A & MCP
Building AI systems that ship — from multi-agent orchestration and production ML to research and prototype.
Center District, Israel
About
I build data science and AI systems that reach production. At Paycor, I own the full lifecycle of ML initiatives and multi-agent platforms — framing problems, prototyping models, and deploying solutions inside real business workflows.
I design agent-native infrastructure on Kubernetes and Terraform: an orchestrator agent coordinates specialized sub-agents over the Agent-to-Agent (A2A) protocol, with Model Context Protocol (MCP) servers exposing tools and context to the agent graph.
Before industry, I spent two years as an algorithms researcher at Ben-Gurion University, developing social network analysis methods on large-scale graphs. Teaching deep learning and algorithms to graduate students sharpened how I communicate complex ideas to both technical and non-technical stakeholders.
I hold an M.Sc. in Data Science from BGU's selective Meitar fast-track program, with a thesis in graph-based bibliographic data analysis. My work spans Python, R, and SQL across machine learning, NLP, graph algorithms, agent orchestration, and statistical modeling.
Teaching and Mentoring
Graduate-level Deep Learning — feed-forward networks, CNNs, and LSTMs for NLP.
Foundations of Algorithms and Complexity — reductions, complexity analysis, and algorithm design.
One-on-one tutoring for Data Structures at Ben-Gurion University.
Experience
Data Scientist at Paycor
Jan 2023 — Present · Center District, Israel
Built production multi-agent infrastructure on Kubernetes and Terraform — an orchestrator agent coordinates specialized sub-agents via the Agent-to-Agent (A2A) protocol.
Integrated Model Context Protocol (MCP) servers as standardized tool and context surfaces for agent workflows.
Own end-to-end data science and AI initiatives — from problem framing and PoC through production deployment.
Design and ship AI-driven solutions for complex HR and workforce analytics at scale.
Data Scientist at getWizer
Jan 2021 — Oct 2022 · Herzliya, Israel
Built and deployed ML models powering a consumer insights SaaS platform used by global brands.
Delivered end-to-end pipelines spanning experimentation, modeling, evaluation, and production integration.
Turned raw survey and behavioral data into actionable product intelligence for marketing teams.
Algorithms Researcher at Ben-Gurion University of the Negev
Oct 2018 — Oct 2020 · Be'er Sheva, Israel
Developed social network analysis algorithms on large-scale graph data to identify influential nodes across multiple application domains.
Implemented high-performance solutions in D, Python, and R.
Teaching Assistant & Tutor at Ben-Gurion University of the Negev
2017 — Oct 2020 · Be'er Sheva, Israel
Faculty member for graduate-level Deep Learning — feed-forward networks, CNNs, and LSTMs for NLP.
Assisted with Foundations of Algorithms and Complexity — reductions, complexity analysis, and algorithm design.
One-on-one tutoring for Data Structures, mentoring students through core CS fundamentals.
Education
M.Sc., Data Science
Ben-Gurion University of the Negev · 2018 — 2020 · Meitar fast-track program
Meitar fast-track program for outstanding students, Department of Industrial Engineering. Thesis: graph-based cataloging of bibliographic data and extraction of structured insights at scale.
B.Sc., Industrial Engineering and Management
Ben-Gurion University of the Negev · 2015 — 2019
Information Systems specialization — combining engineering rigor with data-driven decision making.
Skills
Languages
Python
R
SQL
ML & AI
Machine Learning
Deep Learning
NLP
Multi-Agent Systems
PyTorch
scikit-learn
Agent Systems
Agent-to-Agent (A2A)
Model Context Protocol (MCP)
Agent Orchestration
LLM Agents
Infrastructure
Kubernetes
Terraform
Data Automation
A/B Testing
Specializations
Graph Algorithms
Social Network Analysis
Production ML
Selected Work
Multi-Agent Orchestration Platform
Paycor · Jan 2023 — Present
Production platform for coordinating LLM agents at scale — Kubernetes-native runtime, Terraform-managed infrastructure, and an orchestrator agent that delegates work to specialized agents over A2A.
Designed the agent topology: orchestrator dispatches tasks to domain-specific agents via Agent-to-Agent (A2A) messaging.
Provisioned and managed infrastructure with Terraform; deployed agent services on Kubernetes.
Exposed external tools and data sources to agents through Model Context Protocol (MCP) server integrations.
End-to-end data science initiatives at Paycor — from problem framing and proof of concept through production deployment for HR and workforce analytics.
Own initiatives across the full ML lifecycle, from PoC to production.
Design and ship AI-driven solutions for complex HR and workforce analytics at scale.
Partner with product and engineering teams to integrate models into live business workflows.
Tags: Production ML, HR Analytics, Python
ML for Consumer Insights
getWizer · Jan 2021 — Oct 2022
Built and deployed machine learning models for a consumer insights SaaS platform, turning survey and behavioral data into product intelligence.
Developed ML models powering a platform used by global brands.
Delivered end-to-end pipelines spanning experimentation, modeling, evaluation, and production integration.
Transformed raw survey and behavioral data into actionable intelligence for marketing teams.
Tags: SaaS, ML Pipelines, Python
Bibliographic Data Cataloging with Graphs
Ben-Gurion University of the Negev · 2018 — 2020
Master's thesis exploring graph-based methods to catalog heterogeneous bibliographic records and extract structured knowledge from large-scale library datasets.
Designed graph-based cataloging methods for bibliographic data.
Extracted structured insights from large bibliographic datasets.
Tags: Graph Algorithms, Data Science, Python
Large-Scale Social Network Analysis
Ben-Gurion University of the Negev · Oct 2018 — Oct 2020
Research project developing social network analysis algorithms to identify key actors in massive networks across diverse application domains.
Built SNA algorithms for large-scale graph data.
Implemented solutions in D, Python, and R.
Benchmarked across diverse real-world graph datasets.