דרושים » תוכנה » Data Scientist Expert

We are looking for a talented and experienced Data Scientist to join our Research Data Science team and play a key role in shaping the future of cloud-native network security. Your primary mission will be to lead the development and deployment of AI-driven capabilities that protect enterprise networks at scale through our SASE platform, powering core products such as our AI assitant, DLP, XDR, IPS, and more.
Leveraging rich, real-time data from our global backbone and Cloud data warehouse, you will apply advanced reserach analytics, machine learning, deep learning and GenAI techniques to solve complex cybersecurity and networking challenges.
This is an exciting opportunity to join a fast-growing company and drive innovation in the rapidly evolving SASE space.
Key Responsibilities :
Lead the design, development, and deployment of AI/ML models that enhance our security and networking products
Leading networking and security research, including analysis of large-scale network traffic and security data to identify patterns, threats, and opportunities for product improvement
Research, fine-tune, and train models optimized for real-time inline inference under limited compute resources
Collaborate cross-functionally with product, engineering, and support teams to translate product and business needs into AI solutions
Define and track success metrics to ensure AI solutions meet performance and business goals.

we are seeking an experienced and visionary Data Science Team Lead to join our dynamic technology organization.
The successful candidate will lead a team of talented data scientists, driving innovation and delivering business value through advanced machine learning techniques and Generative AI solutions.
This role requires a strategic thinker with hands-on expertise in both traditional and cutting-edge data science methodologies, exceptional leadership skills, and a passion for continuous learning and development.
Responsibilities:
Lead, mentor, and develop a team of data scientists both methodologically and technically to deliver high-impact projects aligned with business objectives.
Design, implement, and optimize classic machine learning models to solve complex business problems.
Apply gradient boosting techniques (e.g., XGBoost, LightGBM, CatBoost) to enhance predictive accuracy and model robustness.
Drive the exploration and integration of Generative AI applications, including Large Language Models (LLMs) to create innovative solutions for products and services.
Collaborate with cross-functional teams (engineering, product, business) to translate business requirements into actionable data science projects.
Establish best practices for model development, validation, deployment, and monitoring in production environments.
Promote a data-driven culture, encouraging experimentation, sharing of knowledge, and adoption of state-of-the-art technologies.
Communicate project progress, insights, and results to stakeholders at all levels of the organization.

Were looking for a Senior Data Science who is excited about designing and building production-grade AI systems powered by modern LLMs and machine learning.

This role is ideal for someone who enjoys working at the intersection of AI, engineering, and product, and who is passionate about turning cutting-edge AI capabilities into reliable, scalable systems that solve real customer problems.

Youll work closely with product managers, data scientists, and engineers to design, build, and deploy AI-powered solutions - including LLM pipelines, agents, and intelligent automation systems that power our core products.

This is a hands-on role where youll take ownership of the full lifecycle of AI features - from problem framing and architecture design to deployment, evaluation, and iteration in production.

Responsibilities:
Design and build AI-powered systems that leverage LLMs, embeddings, and modern NLP techniques to transform raw product data into structured, actionable insights.
Develop and maintain production-grade AI pipelines including prompt workflows, agents, retrieval systems (RAG), and automated decision processes.
Work closely with product and engineering teams to translate business needs into scalable AI solutions.
Architect systems that combine LLMs, data pipelines, and traditional ML into robust end-to-end products.
Experiment with and integrate new AI tools, models, and frameworks to continuously improve system capabilities and performance.
Own the full lifecycle of AI features - from design and prototyping to deployment, monitoring, and iteration.
Ensure reliability and performance of AI systems in production, including evaluation frameworks, guardrails, and monitoring.
Collaborate across teams to define best practices for AI system design, prompt engineering, and agent orchestration.
Collaborate closely with cross-functional team members, effectively communicate complex ideas, share knowledge, and mentor engineers and data scientists to elevate team standards and impact.

we are seeking an experienced and visionary Data Scientist to join our dynamic technology organization. The successful candidate will be a part of a team of talented data scientists, driving innovation and delivering business value through advanced machine learning techniques and Generative AI solutions. This role requires a strategic thinker with hands-on expertise in both traditional and cutting-edge data science methodologies, and a passion for continuous learning and development.
Responsibilities:
Gradient boosting techniques (e.g., XGBoost, LightGBM, CatBoost) are used to enhance predictive accuracy and model robustness.
Drive the exploration and integration of Generative AI applications, including Large Language Models (LLMs), to create innovative solutions for products and services.
Collaborate with cross-functional teams (engineering, product, business) to translate business requirements into actionable data science projects.
Establish best practices for model development, validation, deployment, and monitoring in production environments.
Promote a data-driven culture, encouraging experimentation, sharing of knowledge, and adoption of state-of-the-art technologies.
Communicate project progress, insights, and results to stakeholders at all levels of the organization. Design, implement, and optimize classic machine learning models to solve complex business problems.

we are looking for a Senior Machine Learning Scientist I.
As a Senior Machine Learning Scientist, you will work closely with engineers and to design, develop, and evaluate machine learning solutions for scalable, customer-facing GenAI applications. Your work will focus on researching, training, fine-tuning, and rigorously evaluating models leveraging LLMs, recommendation systems, and agent-based architectures, using state-of-the-art techniques. You will drive experimentation, define success metrics, and translate insights into impactful AI solutions that shape the future of intelligent travel products.
Key Job Responsibilities and Duties:
Explore and apply state-of-the-art techniques in multimodal machine learning.
Train innovative ML models (NLP, CV, LLM-finetuning), build algorithms, and engineering approaches to drive business impact..
Coding skills: ensure implementation of reusable frameworks (clean and scalable code).
Conduct data analysis with detailed metrics to evaluate models performance, labels quality, features exploration.
Work closely with machine learning engineers to ensure the model's latency/throughput meets product requirements and ensure deployment of your model to production.
Collaborate with multidisciplinary teams: Collaborate with product managers, data scientists, and analysts to understand business requirements and translate them into machine learning solutions.

Our algorithms group is responsible for all of the AI and algorithmic challenges the company is facing, tackling complex real-world problems that directly affect the companys success. As a lead data-scientist, you will lead a new research area and a small team within the company. This is a primarily hands-on role where you will build the AI workflows that digest a large amount of construction documents and architectural models to help with one of the product core processes.
What Youll Do:
Lead and mentor a small team while remaining deeply hands-on in all development aspects-from design to production-of LLM-based workflows to digest information from a variety of construction-oriented documents
Research, design, and integrate LLMs to deliver advanced automation and reasoning capabilities
Develop datasets and benchmarks to evaluate and monitor LLM-based applications effectively
Build LLM evaluation and optimization processes.

We are seeking a hands-on Applied AI Scientist to join our core R&D team and drive the development of next-generation AI systems for autonomous driving. This role sits at the intersection of applied research and deployment - you will go from reading papers to shipping production systems. You will work directly on our multi-layered autonomy architecture, with a primary focus on real-time predictive models for driving decisions.
A deep technical role for someone who thrives on turning cutting-edge research into real, working systems under hard constraints.
Responsibilities:
Own the research-to-deployment cycle for predictive driving models - from literature review and prototyping through to production integration
Design, implement, and iterate on real-time predictive models, including vision-language models, motion prediction models, and inverse reinforcement learning approaches (e.g., imitation learning, reward recovery)
Collaborate on higher-level reasoning systems, contributing to vision-language-action models that handle complex edge cases and long-horizon planning
Bridge cloud-scale training with edge deployment - work on model compression, quantization, speculative decoding, and efficient inference for embedded automotive platforms
Evaluate and integrate state-of-the-art techniques from the broader AI research community into our autonomy stack
Collaborate closely with internal R&D teams to unblock technical challenges, accelerate delivery, and raise the overall technical bar.

The Data Science role is the engine behind our industry-leading Exposure Management (EM) platform.
You will be responsible for the AI models that power our CAASM (Cyber Asset Attack Surface Management) capabilities and our advanced Vulnerability Prioritization and Remediation engines.

We are building the intelligence layer that helps organizations implement a Continuous Threat Exposure Management (CTEM) framework. You will build models that normalize fragmented data from hundreds of sources, calculate complex risk scores, and prioritize which exposures actually pose a threat to the business.

We are looking for an experienced and creative data scientist, with a software development touch, to join our Data Science and our family. A data scientist that understands and loves data, and lots of it.

What you will do...

Solve Applied Product Challenges: Deep-dive into the Armis product ecosystem to identify and solve high-impact problems. You will translate complex customer needs into scalable ML features that directly improve the user experience.
Lead Exposure Intelligence R&D: Develop features that unify asset data (CAASM) with threat intelligence. This includes building models for entity resolution (deduplicating assets across fragmented sources) and automated risk assessment.
Advanced NLP & Knowledge Extraction: Use NLP and LLMs to parse unstructured security data-such as CVEs, threat intel feeds, and security advisories-to automate the mapping of vulnerabilities to specific business contexts.
Predictive Prioritization: Design and optimize algorithms that go beyond static CVSS scores. You will incorporate exploitability (EPSS), reachability, and business criticality to help clients focus on the 1% of exposures that matter most.
End-to-End Ownership: Work closely with Product Managers and Data analysts and Engineers to ensure your models aren't just accurate in a notebook, but are robust, explainable, and deliver clear value within the product UI.
Graph-Based Attack Surface Mapping: Identify hidden patterns and relationships between assets, users, and vulnerabilities to visualize the potential "blast radius" of a security gap.

We are looking for a Data Scientist to join our team.
The ideal candidate is a highly motivated and self-driven individual with strong analytical and problem-solving skills. The Data Scientist will be responsible for analyzing data and developing models to help inform decisions and strategies for new products and business initiatives.
Responsibilities:
Work closely with stakeholders to understand business requirements and develop predictive models to detect fraud.
Develop and implement statistical models, machine learning and deep learning algorithms to solve complex problems in a production environment.
Perform exploratory data analysis and data visualization to gain insights and communicate findings.
Collaborate with cross-functional teams, including data engineers and software developers, to design and implement end-to-end solutions that can be deployed in production.
Implement advanced analytics tools and technologies to enhance reporting capabilities.

As a Research Engineer, you will design, develop, and implement algorithms and models that will enhance the products capabilities, performance, reliability, and scalability. Together with our algo and engineering team members, you will work closely with our product team members to implement algorithms that can be used to drive our business forward as well as the frontier of software development AI technologies.

Responsibilities:

Design, develop, implement and test algorithms and AI-empowered solutions for various product features and applications
Collaborate with cross-functional teams to understand business needs and translate them into algorithmic solutions
Perform research on emerging trends, algorithms, and cutting-edge technologies, and identify ways to incorporate them into our products pragmatically
Propose new and innovative solutions to complex problems and lead the development of algorithms in those areas
Design, cleanse, and utilize benchmarks throughout the process of the algorithm development