דרושים » ניהול ביניים » data Scientist

As a Senior Data Scientist , you will be an integral part of our Machine Learning and AI team. The role is hands-on and impact-driven, focusing on building, improving, and delivering machine learning models that directly support business goals.

Responsibilities
Design and deploy production ML systems that drive automated business decisions (pricing, bidding, resource allocation)
Build end-to-end ML pipelines - from data ingestion and model training to serving, monitoring, and incident response
Translate ambiguous business problems into rigorous mathematical frameworks and own them from conception to production impact
Conduct rigorous experimentation (A/B testing, causal inference, uplift modeling) to measure and improve model performance
Maintain and improve real-time models that adapt to incoming data and feedback signals
Mentor junior team members on ML best practices and production standards

We are seeking a talented and motivated Data Scientist for a temporary position to support our growing data, analytics, and AI automation needs. This role focuses on turning large volumes of data into models, insights, and intelligent automation - combining classic data science (statistical analysis, feature engineering, machine learning) with the emerging Agentic AI stack (LLMs, MCP, agent orchestration). You will work closely with data engineers and internal teams to prototype and productionise models, build LLM-powered agents and workflows, and support the integration of AI capabilities across the organization.

The ideal candidate is passionate about data and AI, comfortable navigating complex systems, and excited by the opportunity to operationalize AI within a modern enterprise environment. We value curiosity as much as experience: we are looking for someone eager to show what they know, and equally eager to keep learning in a field that moves fast.


Responsibilities
Explore, analyze, and model large volumes of structured and unstructured data in Python, from exploratory analysis and feature engineering through to model validation and communication of results.
Design, train, evaluate, and deploy machine learning models, and monitor their performance, accuracy, and drift in production.
Build and orchestrate Agentic AI solutions - LLM-based agents, RAG pipelines, prompt design, and evaluation frameworks - to automate data quality checks, investigation, and reporting workflows.
Integrate models and agents with internal systems and data sources using MCP servers and clients, and workflow automation platforms such as n8n.
Write efficient and maintainable SQL queries to support analysis, reporting, and data exploration needs.
Collaborate with data engineers to productionise models and agents: reliable data flows, logging, alerting, and performance tuning.
Participate in the development of internal tools and dashboards that make data and AI capabilities accessible across the organization.
Share findings with the team and help evaluate emerging AI tooling as the ecosystem evolves.

Are you a talented Data Scientist with expertise in image analysis and text reading? Are you passionate about leveraging cutting-edge technologies to drive innovation in the field of electronic component inspection? Look no further!

Responsibilities
Develop and implement advanced algorithms for image analysis and text reading in the context of component inspection.
Collaborate closely with cross-functional teams to enhance existing models and drive continuous improvement.
Work with large-scale datasets, applying statistical and machine learning techniques to extract meaningful insights.
Stay updated with the latest advancements in the field of image analysis and text reading and apply them to enhance our solutions.

We are looking for an Applied AI Researcher to join our multidisciplinary Antibody team composed of computational biologists, mathematicians and protein designers. In this role, you will drive the development and application of advanced machine learning methods to design and optimize antibodies, contributing directly to cutting-edge therapeutic programs and to the evolution of computational platform.

Develop and apply cutting-edge AI/ML methods for antibody design, including generative modeling and protein language models

Design and implement algorithms for multi-objective optimization of antibodies (e.g., affinity, specificity, stability, developability)

Build tools to support iterative design cycles, combining computational predictions with experimental feedback

Collaborate closely with experimental and computational teams to translate models into impactful design outcomes

Stay current with advances in machine learning, structural biology, and AI-driven protein design

Are you passionate about harnessing cutting-edge data science techniques to protect the world from cyber threats? Do you thrive in the dynamic world of cybersecurity? If so, we invite you to join our innovative and forward-thinking team at Palo Alto Networks, where you'll have the opportunity to shape the future of cybersecurity and impact millions of customers.
As a Senior Principal Data Scientist, you will be a technical leader and hands-on architect, bridging the worlds of advanced data science, robust software engineering, and next-generation AI. You will bring a deep, diverse background in classical machine learning algorithms alongside cutting-edge LLM agent architectures to build resilient, production-grade defense techniques against cyber-villains. Operating as a premier technical authority, you will drive the vision, design, and large-scale development of our groundbreaking, Agentix LLM-based security solutions that block attacks even before they begin.
Key Responsibilities
Serve as a hands-on technical compass for a diverse, elite product-oriented research team, pioneering state-of-the-art technologies and setting the engineering standards for AI/ML excellence across the organization.
Design, implement, and optimize robust Agentic LLM solutions and multi-agent workflows engineered to autonomously counter modern, sophisticated cyber threats.
Oversee and actively contribute to the entire end-to-end lifecycle, moving seamlessly from ambiguous research concepts and POCs to high-throughput, low-latency production deployments.
Build and enforce comprehensive frameworks for continuous real-time monitoring, evaluation, and guardrails to track model drift, latency, and agent behavior in production environments.
Decompose highly complex, multi-layered algorithmic problems into actionable technical roadmaps and strategic insights for both deeply technical engineers and executive business stakeholders.