לוח משרות דרושים Data Scientist

Were looking for a Data Scientist to join our Data Labs department, which specializes in professional services for our super premium customers.
This role will report to our Datalabs Team Manager in the, R&D.
Why is this role so important at our company?
we are a data-focused company, and our unique AI and machine learning capabilities are the center of our business.
As part of this role, you will create data models that help analyze the petabytes of data we receive from various sources, and research and develop new features and capabilities for our product solutions
As a data scientist in the Datalabs team, you will work on the very core of the company. Part of your role will be to turn raw data into usable metrics and develop new models and statistical algorithms to support out-of-the box requests from customers who want custom digital insights. The Datalabs departments customer centric and business-oriented nature also means you will be supporting a team of data analysts and consultants who interact directly with customers. Together with them, you will translate the voice of these customers into best-in-class digital insights.
So, what will you be doing all day?
Your role as part of the R&D department, and as part of the Datalabs team means your daily responsibilities may include:
Working on versatile use cases within a dynamic, fast paced production environment
Strategic thinking and problem solving, with a strong business sense
Analyzing data from various sources, and turning it into usable input
Understanding various use cases from different customers, and creating models that can translate these cases into meaningful insights
Initiating newly defined tasks from exploration to proof of concept
Owning the finalization of defined tasks (from POC to deployment in production)
Collaborating with data engineers to make sure we reach the desired production results
Owning the ongoing validation process for existing metrics
Collaborating and brainstorming best practices with other teams within the R&D department
Deploying machine-learning models into production for an ongoing use.

gTech Ads is responsible for all support and media and technical services for customers big and small across our entire Ad products stack. We help our customers get the most out of our Ad and Publisher products and guide them when they need help. We provide a range of services from enabling better self help and in-product support, to providing better support through interactions, setting up accounts and implementing ad campaigns, and providing media solutions for customers business and marketing needs and providing complex technical and measurement solutions along with consultative support for our large customers. These solutions range from bespoke and customized ones for our customers to scalable support for millions of customers worldwide. Based on the evolving needs of our ads customers, we partner with Sales, Product and Engineering teams within our company to develop better solutions, tools, and services to improve our products and enhance our client experience. As a cross-functional and global team, we ensure our customers get the best return on investment with our company and we remain a trusted partner.
The gTech Ads Data Science EMEA team helps measure and optimize marketing ROI for our companys largest clients. We build bespoke models that address clients key business issues.
In this role, you will be responsible for applying best data science practices to solve our ads customers problems in marketing. You will be passionate about bringing the best of our companys Machine Learning (ML) capabilities to build innovative solutions that address our customer's problem-space. You will be responsible for executing on gTechs strategy and goal to deliver applied data science to our company's largest ads customers.
Responsibilities
Lead data science aspects of client engagements in the area of marketing effectiveness and marketing portfolio management with Machine Learning and statistics.
Collaborate with customers to unpack their problems and identify the best statistical techniques that can solve the problem; own the development of a modeling framework.
Engage important stakeholders to assess data and model readiness and be able to scale a proof-of-concept to a larger solution, collaborate with Product/Engineering teams to increase and optimize capabilities of our data scientist (DS) team, employing methods which create opportunities for scale, proactively helping to drive innovation.
Work with customer and internal teams to translate data and model results into tactical and insights that are actionable for decision.
Develop comprehensive understanding of our company data structures, and metrics, advocating for product changes where needed.

Join the forward-thinking CloudGuard AI Security team, where youll collaborate with leading architects and engineers to design, build, and secure next-generation AI-enabled public cloud environments.
As a Data Scientist, you will not only build and refine machine learning models and analyze large-scale datasets but also take an active part in the coding effort and in developing production-grade components that power CloudGuards AI Security products.
Key Responsibilities
Develop AI Security Insights Leverage advanced statistical and machine learning techniques to detect threats, anomalies, or vulnerabilities in AI-driven cloud environments.
Build, Code & Optimize ML Solutions Design, implement, and optimize ML and LLM-based models using clean, maintainable, and efficient code; integrate them into CloudGuards product pipelines to ensure scalability and reliability.
End-to-End Product Development Collaborate with backend and DevOps engineers to deploy, monitor, and scale ML components within microservices and cloud infrastructure.
Data Exploration & Feature Engineering Conduct deep dives into complex datasets from various sources, transforming raw data into actionable features for robust model development.
Continuous Learning & Adapting Stay current on the fast-paced AI landscape, enterprise use cases, and emerging threat vectors; adapt existing models and pipelines accordingly.
Cross-Functional Collaboration Work closely with other data scientists, product architects, software engineers, and security experts to translate business needs into production-ready, data-driven solutions.

If you're looking for an exciting opportunity to make a significant impact and grow with a passionate team, we are the place to be.
What You Will Do:
Be a core member of a cross-functional AI innovation team, collaborating closely with software engineers, MLOps specialists, and analysts.
Design, build, and optimize end-to-end AI-driven features - from experimentation and model training to validation and deployment.
Develop production-grade code (Python and C#) to integrate advanced models and data-driven capabilities into our core products.
Continuously evaluate model performance and data quality in real-world settings, proactively driving improvements, retraining, and robust monitoring

Required Principal Data Scientist - LLM/GenAI (Cortex)
Your Career
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, where you'll have the opportunity to shape the future of cybersecurity and impact millions of customers.
As a Principal Data Scientist, you will apply your expertise in data analysis, machine learning, and cybersecurity to build new defense techniques against cyber-villains. You will be a driving force in designing and developing groundbreaking, ML-based security solutions that block attacks even before they begin.
Your Impact
Contribute to a diverse and highly skilled research group that pioneers state-of-the-art technologies to protect our customers
Utilize analytical rigor, statistical methods, programming, and data modeling to analyze vast amounts of data, applying your cybersecurity knowledge to guide our focus and approach
Design and implement LLM-based solutions to face modern cybersecurity challenges
Decompose complex problems scientifically and provide actionable insights and recommendations to both technical and non-technical stakeholders
Lead and collaborate on end-to-end projects within the team and across engineering and product teams, from initial ideation to final deployment.

We are seeking a talented and driven Senior Software Verification Engineer to join our innovative team and tackle SW verification challenges in the domains of high-speed networking and virtualization. You will play a key role in validating and testing complex software products that support the InfiniBand protocol, delivering advanced networking services for cloud, HCP machine learning, and AI workloads.

What Youll Be Doing:

Build automated verification tests in a simulated environment for high-end hardware and software.

Collaborate with multiple teams to understand new network technologies, ensuring accurate modeling of complex network behaviors.

Enhance product quality by improving test coverage and developing comprehensive verification strategies.

What will you do:
As Senior Data scientist at our company, you will shape the future of identity security and develop innovative AI/ML solutions that enhance security and protect the emerging agentic AI landscape in top organizations. Drive AI research and engineering to address evolving threats and protect critical digital assets effectively.
Drive AI/ML projects from research to production, leveraging advanced algorithms, LLMs, classification models, Unsupervised learning and optimization techniques to enhance cybersecurity solutions.
Model Building: Design, implement, and optimize machine learning models, including deep learning, using frameworks such as PyTorch, TensorFlow, Hugging Face, and scikit-learn.
Work closely with product manager, engineers, software architects and analysts to understand requirements, validate model assumptions, and deliver actionable insights.
Identify AI/ML impact opportunities and translate business needs into actionable roadmap items.
Stay current with the latest advancements in data science and apply best practices to ensure optimal performance.

As a Data Science Team Leader at our company, you will lead a high-performing team developing AI/ML solutions that secure the digital worlds most sensitive assets. Youll play a key role in driving innovation, productizing machine learning systems, and applying advanced modeling techniques to real-world cybersecurity challenges. In this leadership role, youll balance hands-on technical involvement with strategic oversight, guiding projects from research to production. Youll work closely with data scientists, engineers, analysts, product managers, and Cyber-Security researchers to build intelligent features across our copmpanys identity security portfolio.
Lead, mentor, and grow a team of applied data scientists working on machine learning models that power AI-driven cybersecurity solutions.
Own the end-to-end development lifecycle of AI projects - from exploratory research and experimentation through scalable deployment and optimization.
Partner with engineering and product leadership to define technical strategies, prioritize initiatives, and ensure successful model delivery.
Drive innovation across a broad spectrum of ML domains including supervised/unsupervised learning, anomaly detection and generative AI.
Provide hands-on guidance in model development using tools such as scikit-learn, PyTorch, TensorFlow, and Hugging Face.
Ensure high-quality execution through strong code review practices, reproducibility, and model evaluation frameworks.
Champion best practices in MLOps, data governance, explainability, and monitoring.
Keep pace with academic and industry advances and translate cutting-edge research into productized capabilities.

As a key member in our team, you will manage and lead quality assurance processes, provide actionable insights, and help streamline data monitoring and alert systems. If you have a passion for improving processes, and driving data accuracy in consumer intelligence, we want to hear from you!

Responsibilities:

In-Depth Data QA and Oversight: Continuously evaluate and improve data monitoring and QA processes to enhance team efficiency and data quality. Implement best practices and create standard operating procedures for data verification, analysis, and alert management.

Quality Assurance Supervision: Oversee the regular QA checks conducted on partnership intelligence dashboards to ensure data integrity and consistency. Take a proactive role in resolving discrepancies and ensure the timely delivery of accurate reports.

Data Analysis & Reporting: Lead the analysis of trends and patterns in affiliate performance data to uncover areas for improvement. Prepare and present findings to internal teams in an easily understandable format.

Alert Management Oversight: Supervise the alert management system to ensure relevant alerts are delivered accurately to clients in a timely manner. Work with the team to assess the impact of alerts and determine corrective actions based on client preferences.

Cross-Functional Collaboration: Work closely with internal teams to foster communication and collaboration, ensuring alignment between the QA team and other departments such as R&D.

The ideal candidate's favorite words are learning, data, scale, and agility. You will leverage your strong collaboration skills and ability to extract valuable insights from highly complex data sets to ask the right questions and find the right answers. You should have in-depth knowledge of statistical and data science methodology and tools. Youll join a tight-knit group that values continuous improvement and solving challenging technical and product problems in an emerging space. Plus, we like to have fun along the way.

Responsibilities:
Creating innovative Machine Learning solutions for big data-sparse setups
End-to-end research and development of scalable Data Science solutions for use-case challenges
Maintenance and improvement, if needed, of existing models, including accuracy and performance assessment in production
Collaborate with Data Engineers to tackle scale-up challenges

we are Walmart's first acquisition in Israel, and has become an integrated part of the core of Walmart's e-commerce, the largest retailer in the world!

We develop state-of-the-art NLP solutions that enable millions of Walmarts shoppers to make smarter and more confident buying decisions.

Were growing and are currently looking for a Staff NLP Data Scientist to join our group, and help us through the research and development of cutting-edge algorithms while handling massive amounts of data.

Responsibilities:

Responsible over algorithmic features from inception to production
Research new technologies and adapt them for use in the product
Apply your scientific knowledge and creativity to analyze large volumes of diverse data and develop algorithms to solve complex problems
Write and implement efficient code

As a Data Scientist at our company, you will play a critical role in developing data-driven solutions that uncover legal insights at scale. Youll work across domains-analyzing large and complex datasets, designing models (including those based on LLMs), and translating data into actionable insights that support our mission of surfacing justice.
Youll take full ownership of your analyses and models, from problem definition to experimentation and evaluation. Youll collaborate closely with engineers, domain experts, and product teams to design robust data science solutions that create real-world impact and inform product decisions.
Responsibilities:
Design and implement data science solutions using LLMs and legal datasets to surface insights and automate tasks.
Lead end-to-end development of ML pipelines-from data exploration and preprocessing to model training, deployment, and monitoring.
Build and maintain production-ready pipelines for experimentation, evaluation, and inference.
Collaborate with legal stakeholders and analysts to define problems, identify opportunities, and translate them into data-driven solutions.
Develop and iterate on LLM-based systems, including prompt engineering.
Write clean, maintainable Python code that meets production standards, including testing and documentation.
Stay curious and proactive-evaluating new tools, approaches, and technologies.
Communicate clearly across technical and non-technical audiences, contributing to a collaborative team culture.

Responsibilities:
Data-driven insights. Help identify new products and opportunities for the company in diverse domains.
Develop Machine Learning models. Define requirements & combine data from a wide set of sources to develop ML/DL models that will automate and speed up our business processes.
End to end model management. Conduct in depth research, POC, write production-grade code and implement it (all the way to production) using our MLOps Infra to solve a diversity of business problems.

We are seeking a skilled ML Engineer to design, build, and deploy advanced ML solutions that drive analytics, anomaly detection, and data classification across enterprise-scale environments.
This role will focus on developing and optimizing models, including LLMs and prompt engineering pipelines, and ensuring seamless integration into production workflows spanning cloud-native, on-premises, and Databricks ecosystems.
Key Responsibilities :
Design, build, and deploy ML models for user behavior analytics, anomaly detection, and data classification across enterprise environments.
Collaborate with data scientists, software and data engineers to integrate ML models into production pipelines, cloud-native environments, on-premises, and Databricks workflows.
Develop, fine-tune, and evaluate LLMs and prompt engineering solutions for data classification, labeling, and threat analysis features.
Optimize models using techniques like distillation, quantization, and efficient data structures to boost performance and lower resource cost.
Build high-performance data inputs using embeddings, vector databases, and distributed training frameworks.
Manage model lifecycle and performance via MLOps best practices: monitoring, retraining, and deploying updates.
Partner with data scientists, cybersecurity researchers and product teams to evaluate and refine ML-driven capabilities.
Conduct experiments and benchmark results in fast-paced, data-intensive environments.