לוח משרות דרושים Data Scientist בתל אביב יפו, לחיפוש זה נמצאו 40 משרות

Our Data Science team is seeking a Junior Data Scientist to help build and improve the models at the core of Mizes travel data platform.
What youll do:
Develop and improve ML models
Build LLM-based pipelines for data enrichment, validation and quality checks
Analyze large datasets to find patterns, data-quality issues and opportunities for business impact
Design experiments, define metrics and evaluate model performance in production
Turn findings into clear insights and recommendations for technical and business stakeholders

Required Data Scientist
Tel Aviv
Full-Time
About Us:
Were building the first AI-native autonomous revenue engine for D2C brands, a new category of AI infrastructure replacing the entire revenue stack.
If you want to build category-defining infrastructure with a team that ships weekly, lets talk.
About the Role:
As a Data Scientist, youll be at the heart of the intelligence behind our product. Youll develop predictive models, analyze complex behavioral datasets, and collaborate across engineering and product to turn insights into real-time actions for our clients. This is a hands-on role with plenty of ownership and room to grow.
Responsibilities
Model Development:
Design scalable ML models that predict customer churn, ideal purchase timing, product recommendations, and more.
Data Exploration & Feature Engineering:
Structure and clean large datasets, develop features, and surface key behavioral patterns.
Collaborate with Engineering:
Work closely with developers to implement models and ensure performance at scale.
Business Insight Generation:
Turn data into actionable insights that drive product features and client decisions.

We are looking for an experienced Applied AI/ML Scientist with expertise in building agentic systems and autonomous agents to join one of our R&D. You will be at the core of transforming our supply chain solutions into a fully agentic platform, designing and building agents that autonomously generate analytical pipelines, orchestrate multi-step reasoning, and resolve complex logistics challenges for our customers. You will combine strong machine learning and deep learning expertise with the ability to architect and implement production-grade agentic systems, working closely with engineering, product, and domain experts to push the boundaries of what autonomous AI can do in supply chain.
Responsibilities:
Design and build autonomous agentic systems that generate, configure, and execute analytical pipelines to solve supply chain challenges end-to-end
Architect multi-agent workflows with planning, tool use, memory, and feedback loops, enabling agents to reason, adapt, and improve over time
Develop and integrate ML and deep learning models (e.g., predictive models, anomaly detection, demand forecasting) as core capabilities within agentic pipelines
Research and apply state-of-the-art techniques in agentic AI, LLM orchestration, and multi-agent systems to production use cases
Translate complex logistics and supply chain challenges into agent-based problem formulations, collaborating closely with product and domain experts
Define and implement rigorous evaluation frameworks for agent performance: correctness, reliability, robustness, and edge-case handling
Collaborate with software engineers to productize agentic solutions - including testing, monitoring, versioning, and iterative improvement
Contribute to team practices: reproducible code, experiment tracking, documentation, and knowledge sharing

We are looking for an experienced Applied Data Scientist with expertise in building agentic systems and autonomous agents to join one of our R&D. You will be at the core of transforming our supply chain solutions into a fully agentic platform - designing and building agents that autonomously generate analytical pipelines, orchestrate multi-step reasoning, and resolve complex logistics challenges for our customers. You will combine strong machine learning and deep learning expertise with the ability to architect and implement production-grade agentic systems, working closely with engineering, product, and domain experts to push the boundaries of what autonomous AI can do in supply chain.
Responsibilities:
Design and build autonomous agentic systems that generate, configure, and execute analytical pipelines to solve supply chain challenges end-to-end
Architect multi-agent workflows with planning, tool use, memory, and feedback loops - enabling agents to reason, adapt, and improve over time
Develop and integrate ML and deep learning models (e.g., predictive models, anomaly detection, demand forecasting) as core capabilities within agentic pipelines
Research and apply state-of-the-art techniques in agentic AI, LLM orchestration, and multi-agent systems to production use cases
Translate complex logistics and supply chain challenges into agent-based problem formulations, collaborating closely with product and domain experts
Define and implement rigorous evaluation frameworks for agent performance: correctness, reliability, robustness, and edge-case handling
Collaborate with software engineers to productionize agentic solutions - including testing, monitoring, versioning, and iterative improvement
Contribute to team practices: reproducible code, experiment tracking, documentation, and knowledge sharing

As a Staff Data Scientist your role will be to:

Use analytical, statistical and technical skills to understand large, complex datasets with the aim to eliminate waste and measure incrementality for advertisers.
Lead, Develop & improve algorithms for automated decision making around lifetime value, causality, multi-touch attribution, and media mix optimization
Determine, analyze, maintain, and report on core marketing metrics to identify causal relationships between marketing actions and outcomes.
Translate data into actions and recommendations - appropriately interpreting and building on findings, and fully exploiting insights

As a Data Scientist you will be working with colleagues from various disciplines - Research Engineers, Analysts, and other Data Scientists - on statistical methods and machine learning techniques and Gen-AI (both supervised and unsupervised) in order to develop classification and anomaly detection algorithms.

Were looking for a Data scientist Expert to join us and spread the power of our company. As a Data Scientist you will be responsible for driving research and development of autonomous AI agents and LLM-powered systems at our company. You will work closely with cross-functional teams to explore, innovate, and implement AI-driven solutions to tackle emerging threats, examine cloud security features, and enhance the companys security posture.
Responsibilities
Lead applied research on AI agents and LLM-driven features in the company platform - from autonomous threat investigation agents to AI-powered security operations workflows.|Cover a range of features - from leveraging LLMs to enhance customer investigation experience to novel usage of AI for cloud security.
Collaborate with engineering teams to design, build, and maintain production pipelines.
Work closely with the Security Research and Product teams to define research goals.
Conduct experiments and evaluate the performance of AI models, algorithms, and techniques using real-world datasets and simulated environments.
Stay abreast of cutting-edge AI methodologies, frameworks, and tools and apply them to improve security solutions' accuracy, efficiency, and scalability.

We're looking for a Senior Data Scientist who loves what they do: a strong foundation in machine learning, statistics, and software engineering, a passion for modern AI-accelerated development, and the drive to own research, design, and end-to-end delivery of ML systems that ship to millions.

What you'll be doing


Research, train, and deploy state-of-the-art recommender systems using deep learning and GenAI
Own projects end-to-end - from business idea through modeling, experimentation, and production deployment - including the parts that involve aligning other people around them
Build online, scalable models and serving solutions that leverage massive datasets and reach hundreds of millions of users
Apply multimodal and sequence-modeling techniques to real product problems where model improvements translate directly to business impact
Lead cross-team initiatives, improve the tooling the team works with every day, and mentor others as you go
Collaborate with ML experts, big data engineers, product managers, and developers across the organization to solve complex problems

We are hiring an Applied Data Scientist to build the models that turn raw transaction data into the insights our banks customers see every day.
The stack is deliberately wide: classical ML on tabular data, deep learning, LLM-based systems, and agentic flows. We choose the right approach for each problem, balancing analytical quality, production readiness, and time to market. You drive the analytical work, working closely with Product and Engineering to take ideas from evidence through production.
Responsibilities:
Analyze and interpret complex datasets to identify trends and patterns.
Collaborate with cross-functional teams to develop data-driven solutions.
Build and maintain predictive models to enhance business decisions.
Communicate findings and insights effectively to stakeholders.
Stay updated with the latest data science technologies and methodologies.
Contribute to the continuous improvement of data processes and systems.

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.

Required Part-Time Research Scientist PhD Intern, 2027
Please complete your application before 23rd October 2026. We encourage you to apply as early as possible as we review applications on a rolling basis.
This internship is intended for students in their penultimate/final year of education in an accredited full-time PhD degree program in Israel. Participation in the internship program requires that you are located in Israel for this role for the duration of the internship program. Interns will be required to be available to work part-time (minimum 2 days a week, maximum 4 days a week) alongside your studies for a minimum of 26 weeks starting early 2027 (starting in March, April or May) for the duration of the program. Individuals applying for this position will not be eligible for immigration sponsorship.
About the job
Research happens everyday, on many different embedded teams throughout the company. Our research reaches the user through both services and products such as Search, Maps, Assistant, Translate, Cloud, and our computing, storage, and networking infrastructure. To achieve this, were working on projects that utilize the latest technologies that push the boundaries of what is possible. Research-focused engineering interns are embedded throughout the company, contributing to the setup of large-scale tests and deploying promising ideas quickly and broadly. Ideas may come from internal projects as well as from collaborations with research programs at partner universities and technical institutes all over the world. From creating experiments and prototyping implementations to designing architectures, research-focused engineering interns work on real-world problems including artificial intelligence, data mining, natural language processing, hardware and software performance analysis, improving compilers for mobile platforms, as well as core search and more.
Our Research addresses challenges that define the technology of today and tomorrow. From conducting fundamental research to influencing product development, our research teams have the opportunity to impact technology used by billions of people every day.
Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field -- we publish regularly in academic journals, release projects as open source, and apply research to our products.
Responsibilities:
Participate in research to develop solutions for real-world, large-scale problems.
Research, conceive, and develop software applications to extend and improve on our product offering.
Contribute to a wide variety of projects utilising natural language processing, artificial intelligence, data compression, machine learning, and search technologies.

Required Research Data Scientist II, Waze
About the job
The Personalized Experience team aims to give Waze drivers personalized, hyper-local, contextual and timely routing experience. The team contributes to the Personalized Navigation squad and more. The team members are embedded within the Waze development Squads. We develop and own Machine-Learning models from ideation to production, including research, production grade pipelines, feature engineering, experimentation, monitoring and dashboards, inspection and debugging.
The team works closely with the product team, helping to shape the requirements and success metrics. The team works closely with the Backend teams, integrating the models, ensuring a feedback loop to enrich the models, triage and solve users complaints, leading and guiding experimentation and more.
Waze is where people and technology meet to solve transportation challenges. It's a platform that empowers users to contribute road data and edit Waze maps to improve the way we move about the world. As the social navigation pioneer, Waze leverages mobile technology and a passionate global community to redefine expectations of todays maps.
Responsibilities
Feature engineering, model evaluation and error analysis, data processing and pipelines, model tuning, monitoring and dashboards.
Work with product managers, software developers and quality analysts to define requirements, formulate success metrics and lead experimentation.
Use data to help products to shape the future of Waze and drive innovation. Provide insights on user behavior and help engineers and quality analysts detect and solve complex problems.
Utilize Cloud Platform, Vertex AI, Python, Scikit-learn, TensorFlow, Airflow/Composer, Python and Looker tools.

This role combines deep technical expertise with leadership responsibility, driving multidisciplinary Data Science initiatives end-to-end within complex security policy management systems.

The role drives the development of innovative, production-grade AI capabilities, including the intelligence behind security AI agents and advanced machine learning models built on complex security data.

Original thinking, deep technical rigor, intellectual agility, and exceptional problem-solving are essential.

Responsibilities:

Lead end-to-end Data Science initiatives from problem framing through validation, CI/CD-based production deployment, monitoring, and ongoing operational optimization of AI systems

Develop advanced ML capabilities, including predictive modeling, anomaly detection, classification, and behavioral analysis

Develop the intelligent capabilities behind security AI agents, combining machine learning, LLMs, statistical methods, and domain-specific algorithms, with a strong understanding of how agents use these capabilities within multi-step workflows

Adapt and fine-tune LLM technologies for domain-specific security use cases

Define and implement rigorous evaluation methodologies for ML and agentic AI systems, including decision quality, reliability, robustness, uncertainty, and failure modes

Partner with Product, Engineering, and Security teams to deliver measurable business impact

Provide technical leadership and mentorship across multidisciplinary Data Science initiatives

Required Customer-Facing Data Scientist - Demand Forecasting
Demand Forecasting Team:
The Demand Forecasting team is at a pivotal, high-growth stage. We have successfully built our core pipeline and are now aggressively onboarding new, diverse customers. This role is highly dynamic, requiring a blend of technical depth, proactive research, and exceptional communication to adapt our platform to real-world customer data and challenges. You will be a critical link between the core product and our clients business success.
Responsibilities:
As a key Customer-Facing Data Scientist on the Demand Forecasting team, you will:
Work at the forefront of the data world, focusing on the optimization and deployment of Demand Forecasting models.
Handle complex data science challenges using state-of-the-art techniques relevant to demand forecasting and time series, such as forecasting model ensembling, anomaly detection, feature engineering for trend/seasonality, and causal inference modeling.
Act as a highly communicative partner to our clients, diving deep into their unique datasets to understand the nuances and challenges of their business.
Lead the rigorous testing and application of our core ML pipeline on new customer data, identifying areas where it performs exceptionally well and researching/developing solutions for complex cases where it does not.
Be instrumental in helping our clients achieve their business goals and realize the measurable value of our Demand Forecasting module.
Work within a production and product-oriented environment, ensuring models are robust, scalable, and directly integrated into the clients operational workflow.