דרושים » תוכנה » Data Scientist

we are disrupting performance marketing, delivering millions of new customers to brands every month. We're hiring a hands-on Senior Applied Data Scientist to help build and scale production-grade recommendation systems that drive our core marketplace outcomes.
This is not a research-only role. We're looking for someone who can take models from idea to production - running experiments, measuring business impact, and continuously improving the systems behind how our company matches people with the right brands. You'll own the full lifecycle: exploratory analysis, data prep, modeling, testing, deployment, and post-launch measurement.
The ideal candidate has worked in a real production environment, brings strong deep learning experience, and understands recommendation systems in practice - not just in theory. Success here means shipping models that move our company's core KPIs, connecting technical work to measurable business impact, and helping the team scale with strong engineering discipline.
This role is ideally based in Israel, but strong candidates in the U.S. will also be considered.
What You'll Do
Own the full funnel of applied machine learning work, from idea through production
Build, improve, and deploy recommendation models that support our company's core business goals
Tackle deep learning problems in a production setting - not just offline experimentation
Conduct exploratory data analysis, preprocessing, feature development, and modeling
Run experiments and evaluate success against business KPIs, not just model metrics
Partner with engineering and infrastructure teammates to productionize models and scale systems
Improve recommendation quality, personalization, and the business performance tied to those systems.

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.

As a Fraud Data Scientist, you will be at the front lines of protecting our ecosystem from sophisticated financial fraud and abuse. You will join a high-impact team operating in a data-rich, high-frequency environment where seconds matter.
In this role, you will take ownership of the end-to-end machine learning lifecycle-from uncovering complex fraud patterns to deploying highly scalable, real-time models into production. You will collaborate closely with Engineering, Product, and Risk Operations to build robust defenses that balance strict security with a seamless user experience.
What You Will Be Doing
Model Development & Deployment: Design, train, and deploy advanced machine learning models (e.g., gradient boosting, anomaly detection, graph networks) to detect and mitigate fraud in real-time.
Production Ownership: Take full ownership of putting models into production systems, ensuring low-latency execution and high reliability.
Agentic Workflows: Research, build, and implement Agentic flows and LLM-driven orchestration to automate multi-step fraud decisioning, logic routing, and investigation paths.
Adversarial Analysis: Conduct deep-dive exploratory analysis on massive datasets to identify emerging fraud vectors, loops, and coordinated attacks.
Feature Engineering: Build and optimize real-time streaming and batch features to improve model signal and precision.
Experimentation & Monitoring: Design rigorous shadow-testing and A/B testing frameworks for new models. Set up continuous monitoring pipelines to catch data drift and performance degradation early.

Were looking for an experienced, rigorous Data Scientist to join our monetization data team in Tel Aviv. Youll lead dynamic price-floor optimization strategies for programmatic display advertising across our global inventory, build models and experiments that determine which on-page experiences we show each user, and develop strategies to maximize user lifetime value while strengthening engagement and retention. Youll also shape and manage the roadmap for our internal experimentation platform, enabling rigorous testing across the business. This is publisher-side data science, focused on our own audiences and those of our partners. The bar that matters most here is measurement, proving credibly that a change actually moved revenue before it ships.


Responsibilities:

Design and build models for supply-side monetization optimization, user lifetime-value, retention, and ARPU predictions, while measuring revenue impact.

Analyze large-scale event data and maintain metric foundations.

Partner with cross-functional teams to deploy models into production.

Communicate findings and business impact to stakeholders.

Provide technical leadership and mentorship.

Monitor industry trends in programmatic advertising and ad-tech.

We are looking for an experienced Data Product Manager with a strong Data Science and Analytics background to own and lead data driven solutions.
In this role, you will define and drive the vision, strategy, and execution of data and analytics products that support our Marketing, Growth, and Acquisition teams. You will operate at the intersection of business, analytics, data science, and AI, translating marketing challenges and business questions into scalable data products, clear KPIs, and measurement frameworks that improve acquisition efficiency, ROI, and long-term revenue growth.
You will lead cross-departmental initiatives, partner closely with Marketing leaders, Data Scientists, Data Engineers, Analytics teams, and Product Managers, and ensure that advanced modeling, analytics, and AI capabilities are deeply embedded into day-to-day marketing decision-making.
Key Responsibilities
Marketing Data Product Ownership
Own the vision, roadmap, and delivery of Marketing Data Science and Analytics products
Align initiatives with business goals such as acquisition efficiency, ROI optimization, revenue growth, and funnel performance.
Act as the single point of ownership for marketing-related data, analytics, and data science products across teams.
Define data requirements, metrics, and success criteria for new initiatives, including identifying gaps in existing data.
Collaborate with Data Engineers and Analytics Engineers to design scalable data pipelines, data models, and semantic layers that support analytics, experimentation, and advanced modeling.
Prioritize initiatives based on business impact, scalability, data maturity, and clarity of success metrics.
Business Analytics, Data Science & AI-Driven Marketing Optimization
Define and own the business logic behind marketing KPIs, ensuring clear, consistent, and trusted metrics across teams.
Work closely with stakeholders to understand what needs to be measured, why and how.
Lead data science, analytics, and AI initiatives that optimize marketing performance
Own and evolve core marketing models including attribution, MMM, LTV prediction.
Define success metrics and measurement frameworks for models, experiments, and AI-driven decisions.
Drive experimentation frameworks (A/B tests, geo-experiments, uplift modeling) to continuously improve performance and decision quality.
Design and deploy AI-powered and agentic solutions to automate and scale marketing workflows such as budget allocation, campaign optimization, and creative testing.
Ensure solutions are production-ready, measurable, explainable, and actionable for business stakeholders.
Translate complex model outputs into clear, trusted insights for business users.

We are looking for a Senior Data Scientist to lead the design and implementation of next-generation AI solutions for Lifecycle Marketing.
As a senior member of our team, you will shape methodology, guide product direction under uncertainty, and represent in high-stakes conversations with customers. Youll combine deep expertise in causal inference, reinforcement learning, and experimentation with strong communication skills to drive impact across the company and with our clients.
Responsibilities:
Own the design and methodology of data science solutions for lifecycle marketing, with emphasis on causal inference, uplift modeling, and reinforcement learning.
Lead the development of experimentation frameworks (A/B/n tests, variance reduction, policy evaluation).
Research, prototype, and productionize multi-armed bandits and contextual RL approaches for personalization and timing optimization.
Take a strong, opinionated role in brainstorming sessions, guiding decision-making in areas of high uncertainty.
Represent Voyantis in front of customers, translating complex modeling decisions into clear business value.
Mentor junior and mid-level team members, raising the technical bar across the team.
Collaborate closely with product managers, engineers, and analysts to align data science strategy with business goals.

we are looking for a Senior Data Scientist
As a Senior Data Scientist on the Algorithms team in our Tel Aviv office, youll play a vital role in designing and deploying machine learning models that power real-time bidding and optimize billions of daily auctions.
Youll develop advanced modeling solutions that drive bid optimization, price prediction, and auction strategy while partnering closely with Engineering, Product, and Business teams to bring research into production.
Your work will directly improve advertiser performance, publisher revenue, and marketplace efficiency at massive scale.

we are looking for a AI Applied Scientist - Insights & Recommendations.
As AI Applied Scientist, youll be at the intersection of advanced technology and meaningful impact with clear business cases. You will have access to real-time (RT) big data from multiple Sensors & Data sources, and a chance to discover, explore research, and develop cutting-edge models that directly impact industrial manufacturers around the globe. As part of the Applied AI/ML Science team , you will have the opportunity to continuously grow and learn while building and deploying advanced models directly into our products, and stay at the forefront of the world's technologies, witnessing firsthand their impact on our customers.
A Day In Your Life:
Own the algorithm lifecycle from problem definition and data analysis to prototyping and delivering production-ready models.
Combine classic methods with inference techniques: statistics, Time series, Deep learning, anomaly detection, Recommendation Systems, Transformers, and Inference to extract data-driven insights.
Research, design, and build Agentic applications on top of sensor time-series data, textual data, images, ML applications, and other various data sources.
Engage with customers and collaborate with our product team to develop innovative solutions utilizing new types of data.
Leverage modern technologies: work with cloud-based big data platforms for storage, distributed processing, LLMs and agents (GPT, Claude, Gemini), defining & building Gurdrails, reasoning chain as function call, planning, ML-based vectorization and embeddings, and stream analysis.

we are looking for a Research Scientist - Sovereign AI Research.
We are hiring across the following specializations. We expect depth in at least one area and the intellectual range to collaborate across them.
Computer Vision: object detection, segmentation, multimodal grounding, vision-language models, contrastive and self-supervised representation learning, low-resource and few-shot visual recognition.
NLP / Speech: LLMs, NERs, relation extraction, span-based and generative IE, semantic textual similarity, multilingual and cross-lingual transfer.
Reinforcement Learning: MDPs, POMDPs, model-based and model-free RL, Online Offline methods, reward modeling, sim-to-real transfer, compute-aware planning.
Graph Learning: GNNs, graph clustering, community structure, generative methods, knowledge graph embeddings, dense and sparse semantic retrieval.
Optimization: convex and nonconvex optimization, constrained and Lagrangian methods, combinatorial and integer programming, knowledge distillation (response, feature, and relation-based), test-time optimization, Bayesian optimization, resource-aware inference.
Representation Learning: contrastive learning, self-supervised and unsupervised pre-training, disentangled representations, metric learning and embedding spaces, cross-modal and multimodal alignment, meta learning (hypernetworks), transfer learning and domain adaptation, probing and interpretability of learned representations, world models.
Neurosymbolic AI: neuro-symbolic integration, differentiable theorem proving, inductive logic programming (ILP), probabilistic soft logic (PSL), causal inference and structural causal models (SCMs), programmatic and compositional reasoning
Responsibilities:
Define and execute a research within your track, experiments, quality gates, and upper bounds in rigorous manner.
Collaborate across tracks on system integration and cross-disciplinary research.
Collaborate work with engineering teams until production.
Mentor junior researchers and contribute to a culture of technical excellence