לוח משרות דרושים Data Scientist במרכז, לחיפוש זה נמצאו 72 משרות

Were seeking a self-motivated Data Analyst with a strong sense of ownership and outstanding communication skills. As a key team member, you will play a crucial role in analyzing and deriving insights from product and marketing data to drive strategic decision-making. You will collaborate with various teams across the organization to optimize business processes, create and maintain BI reports and dashboards, and contribute to the companys data-driven culture.

You'll work directly with senior, data-driven stakeholders across the company, including executives outside Israel. Expectations are high, feedback is direct, and the work is visible. In return, you get real ownership, real impact, and the kind of proximity to business decisions that most analyst roles don't offer.


How Youll Spend Your Time
Surface actionable insights by identifying business trends, performance drivers, and anomalies across product, marketing, and financial data
Analyze large, complex datasets to answer strategic business questions and directly support decision-making across the organization
Define, track, and improve the KPIs that measure business and operational performance, ensuring stakeholders trust the numbers
Translate data into compelling visualizations and narratives that communicate key metrics, trends, and recommendations to business stakeholders
Drive product decisions through analytics and experimentation. Design and analyze A/B tests, measure feature adoption and funnel performance, and translate user behavior into insights that shape the roadmap
Build and maintain the data warehouse infrastructure, ETL pipelines, table structures, and schema design that powers reliable, timely analytics
Act as a subject-matter expert in analytics and BI tools, guiding stakeholders and team members toward better data-driven decisions
Own financial and transactional reconciliation. Validate revenue and performance metrics against partner-reported data at cent-level precision
Use AI tools effectively as a core part of the workflow, while maintaining a deep understanding of the data and code underneath

We are looking for a Senior Data Scientist to lead the development of machine learning solutions powering real-time decision-making and recommendation systems in industrial production environments.

This is a hands-on role with end-to-end ownership. You will be responsible for the full modeling lifecycle, from problem framing and data exploration to modeling, evaluation, deployment, and production impact. You will work with complex, noisy sensor and control data, building models that must be robust, interpretable, and reliable in real-world conditions.

You will collaborate closely with deep learning researchers, research engineers, and domain experts, and play a key role in shaping how data science is applied across the system.
Key Responsibilities


Design, develop, and evaluate machine learning models for real-time industrial applications
Own the end-to-end modeling pipeline, including problem formulation, data preparation, feature engineering, model development, evaluation, deployment, and post-production monitoring
Translate ambiguous production challenges into well-defined data science problems
Lead feature engineering and data exploration on complex sensor and control datasets
Define KPIs, evaluation frameworks, and experimentation strategies
Ensure model robustness over time, including monitoring, drift detection, and continuous improvement
Work closely with data and research engineers to bring models into production
Drive best practices in modeling, validation, and reproducibility
Communicate insights, trade-offs, and recommendations clearly to stakeholders

We are hiring an Engineering Manager to own and grow the Data Science group - the R&D team behind our company's security and safety AI models and the data platform that powers them. You will lead a multidisciplinary team of ML engineers, data scientists, and software engineers who ship real-time inference at scale, automated red-teaming of GenAI systems, and the Databricks/Spark data platform underneath. This is a hands-on people-leadership role: you set technical direction, are accountable for delivery and quality, and you build and grow the team.
What your team owns
The group is responsible for a large Python + Rust + PySpark monorepo (dozens of production services and shared libraries) spanning three connected domains:
1. Content-moderation inference at scale
Real-time, multi-tenant detection across text, image, video, and audio - hate speech, CSAM, nudity, child grooming, extremism, PII, prompt injection, age estimation, and more - served through an in-house ActiveServe framework over NVIDIA Triton and a Rust detection monolith, on latency-sensitive, SLA-bound, customer-facing traffic with per-customer custom models.
2. GenAI safety & red-teaming
Automated red-teaming that attacks customers' LLM applications with a research-driven attack taxonomy and measures attack-success rate, alongside the defensive side - LLM-as-judge escalation to cut false positives and the tooling that authors and refines moderation policies - built on a multi-provider LLM foundation (Bedrock, Anthropic, OpenAI, Gemini, xAI Grok) and forming our company's leading edge into agentic-AI safety.
3. Data platform & MLOps
An end-to-end lakehouse and MLOps stack on Databricks - bronze/silver/gold ingestion, PySpark pipelines, and model training, versioning, and promotion through MLflow / Unity Catalog into Triton serving - with the performance-critical hot paths engineered in Rust (PyO3/maturin) for sub-millisecond, high-QPS matching and detection.
What you'll do
Lead and grow the team - mentor ML engineers, data scientists, and software engineers, and own hiring, onboarding, 1:1s, career development, and performance.
Set direction and deliver - set technical direction and standards, turn company and product goals into a prioritized roadmap, and own the quality, reliability, and delivery of the systems above across parallel workstreams.
Champion excellence and partnership - stay hands-on to review designs and unblock the team, drive engineering excellence (testing, observability, CI/CD, on-call, cost/latency), keep the team at the state of the art in ML, LLMs, and GenAI safety, and partner with Product, Platform, and GenAI-safety stakeholders.
Leadership competencies
People-first: builds trust, grows engineers, and creates a healthy, inclusive, high-ownership culture.
Outcome-oriented: drives clarity, sets priorities, and delivers under ambiguity without micromanaging.
Technical credibility: earns the team's respect through sound judgment on architecture and trade-offs.
Systems thinker: balances short-term delivery against long-term platform health, cost, and tech debt.

Design, implement, and train state-of-the-art neural network architectures for robotic applications, including large multimodal models (vision, language, sensor data) and generative models.
Architect and maintain scalable deep learning frameworks for training on massive, real-world robot datasets.
Develop and implement cutting-edge techniques in areas such as imitation learning, reinforcement learning, self-supervised learning, and video-based models.
Own the full machine learning lifecycle: define data collection and labeling strategies, build high-throughput data pipelines, and establish robust evaluation and deployment protocols.
Collaborate closely with software, hardware, and controls teams to integrate and test your models on physical robots.
Leverage and contribute to internal tooling for large-scale data processing, model experimentation, and continuous integration.

We are seeking an agile, hands-on R&D Lead who excels at rapid learning and making smart, pragmatic decisions to drive our core data engineering squad in Tel Aviv. We care less about years spent in formal management or having a decades-long resume, and much more about your ability to take extreme ownership, learn new paradigms on the fly, and keep the team moving forward.


In this role, you will help architect, write, and deploy the Python-based infrastructure that processes millions of records daily and serves them at sub-second latency to autonomous AI systems. You will guide the design of high-scale data pipelines, integrate semantic search paradigms, and build out our agentic connectivity layer. If you are a proactive decision-maker who thrives on optimizing complex systems and wants to build the infrastructure that LLMs rely on to understand the real world, this is your next role.


Key responsibilities
Execution & Decisive Leadership: Lead the technical delivery by setting the pace and making pragmatic architectural choices that keep the team moving forward. You prioritize shipping and iterating over analysis paralysis.
Hands-On Development (Python): Roll up your sleeves and remain technical. Write clean, concurrent, and scalable Python code to handle data ingestion, transformation, and serving pipelines.
Continuous Learning & Tech Agility: Work across a polyglot environment. You dont need to know every tool on day one, but you must be eager to rapidly evaluate, learn, and adopt the optimal databases, message brokers, and execution frameworks to manage technical debt.
Agentic Infrastructure & MCP: Deploy systems that allow autonomous agents to seamlessly query our datasets. You will help build and scale our Model Context Protocol (MCP) servers, translating LLM intents into highly optimized database queries.
High-Scale Data Pipelines: Oversee an end-to-end data architecture capable of supporting continuous ingestion, error cleansing, and near-real-time refreshing of a 1B+ record database while enforcing GDPR and CCPA compliance.
Search & Retrieval Integration: Design and optimize advanced search capabilities, leveraging vector databases, embeddings, and hybrid search techniques to ensure absolute precision when AI models query our data.

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 experienced Analytics Engineer to join our Clinical Value team and develop the data foundations, infrastructure, and analytical tools that enable our work.
This hybrid role combines Analytics Engineering with hands-on data analysis. You will translate complex analytical and clinical needs into scalable, reliable, and reusable data solutions.
You will design data models, pipelines, analytical layers, and tools that enable complex Clinical Value studies across products and customers, while remaining hands-on with selected analytical projects.
You are expected to be independent, proactive, and impact-oriented, taking end-to-end ownership of technical challenges and opportunities for improvement.
evaluation.
Responsibilities:
Design, build, and maintain the data infrastructure supporting Clinical Value analyses, including reusable data models, pipelines, analytical layers, and tools.
Own the technical and analytical implementation of complex Clinical Value applications, adapting established methodologies to different products, customers, and real-world data environments.
Develop complex analytical solutions into reusable and scalable frameworks, enabling broader and more efficient execution across the team.
Identify data-quality issues, technical bottlenecks, and repetitive processes, and develop solutions and automation to improve reliability and efficiency.
Work closely with analysts, Data Engineering, and other technical teams to translate analytical needs into effective, maintainable data solutions and establish best practices.
Lead selected hands-on data-analysis projects, analyzing complex clinical and operational datasets to generate credible, actionable conclusions.
Collaborate with Go-to-Market teams, including Customer Success, to understand customer needs and support impactful customer-facing value demonstrations.

We are hiring an Engineering Manager to own and grow the data Science group - the R&D team behind security and safety AI models and the data platform that powers them. You will lead a multidisciplinary team of ML engineers, data scientists, and software engineers who ship Real-Time inference at scale, automated red-teaming of GenAI systems, and the Databricks/Spark data platform underneath. This is a hands-on people-leadership role: you set technical direction, are accountable for delivery and quality, and you build and grow the team. What your team owns The group is responsible for a large Python + Rust + PySpark monorepo (dozens of production services and shared libraries) spanning three connected domains: 1. Content-moderation inference at scale Real-Time, multi-tenant detection across text, image, video, and audio - hate speech, CSAM, nudity, child grooming, extremism, PII, prompt injection, age estimation, and more - served through an in-house ActiveServe framework over NVIDIA Triton and a Rust detection monolith, on latency-sensitive, SLA-bound, customer-facing traffic with per-customer custom models. 2. GenAI safety & red-teaming Automated red-teaming that attacks customers' LLM applications with a research-driven attack taxonomy and measures attack-success rate, alongside the defensive side - LLM-as-judge escalation to cut false positives and the tooling that authors and refines moderation policies - built on a multi-provider LLM foundation (Bedrock, Anthropic, OpenAI, Gemini, xAI Grok) and forming leading edge into agentic-AI safety. 3. data platform & MLOps An end-to-end lakehouse and MLOps stack on Databricks - bronze/silver/gold ingestion, PySpark pipelines, and model training, versioning, and promotion through MLflow / Unity Catalog into Triton serving - with the performance-critical hot paths engineered in Rust (PyO3/maturin) for sub-millisecond, high-QPS matching and detection. What you'll do
* Lead and grow the team - mentor ML engineers, data scientists, and software engineers, and own hiring, onboarding, 1:1s, career development, and performance.
* Set direction and deliver - set technical direction and standards, turn company and product goals into a prioritized roadmap, and own the quality, reliability, and delivery of the systems above across parallel workstreams.
* Champion excellence and partnership - stay hands-on to review designs and unblock the team, drive engineering excellence (testing, observability, CI/CD, on-call, cost/latency), keep the team at the state of the art in ML, LLMs, and GenAI safety, and partner with Product, Platform, and GenAI-safety stakeholders. Leadership competencies
* People-first: builds trust, grows engineers, and creates a healthy, inclusive, high-ownership culture.
* Outcome-oriented: drives clarity, sets priorities, and delivers under ambiguity without micromanaging.
* Technical credibility: earns the team's respect through sound judgment on architecture and trade-offs.
* Systems thinker: balances short-term delivery against long-term platform health, cost, and tech debt.
 
 
 
the GenAI safety and security platform that keeps online experiences safe. We provide AI-driven detection, moderation, and red-teaming that protect billions of users across social platforms, marketplaces, gaming, and - increasingly - the GenAI applications reshaping the internet. Our data Science group builds the machine-learning and data systems at the core of that mission.

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

we are searching for the best Data Scientist out there!
We are a new startup founded by successful serial entrepreneurs with the vision to evolve digital marketing from the measurement of traffic to measuring VALUE.
Advertising for the past 100+ years has faced a strange dilemma: Half the budget I spend on Advertising is wasted. The problem is - I dont know which half (John Wanamaker, 1838-1922)
We are a deep machine learning and AI company, using the latest technologies, to solve a really simple question - which half is wasted?
We are a new startup, launched in summer of 2020, backed by strategic investors from around the world who believe in what were doing.
We have customers and a constant flow of interest from the press, investors and more large companies who want to join us.
Role Description:
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
You will also participate in important decisions around the product, our strategy as well as have the opportunity to choose the areas you work on based on what you want or like to do the most. We believe in helping each other get most of ourselves rather than do what were not interested in doing.

We're looking for a Machine Learning Team Lead to own real parts of it.
What you'll actually do?
Lead, mentor, and grow a multidisciplinary team composed of Data Scientists, Data Analysts, and DLP Analysts.
Define and lead the team's technical vision and execution strategy, driving technical excellence and ensuring alignment with business priorities and product objectives.
Oversee the design, development, validation, and deployment of advanced machine learning solutions for customer-facing products.
Hands-on ownership of current AI services.
Establish and enforce best practices for model development, experimentation, evaluation, documentation, and code quality.
Collaborate closely with Product Managers, Data Engineers, Software Engineers, and domain experts to translate business challenges into scalable ML solutions.
Drive execution and delivery, balancing innovation, technical quality, and time-to-market considerations.
Promote operational excellence by improving the reliability, scalability, and maintainability of production machine learning systems.
Establish scalable engineering processes, workflows, and best practices.
Lead the team transformation from an early-stage organisation into a mature, production-grade AI engineering team.

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.

We're looking for a Senior Data Product Manager to lead our analytics and insights platform. At the core of the role is owning the story data tells: speaking the language of C-suite and senior executives, and translating complex data into clear insight and business impact, helping health system leaders gain unique insights on their business and their patients. You're a true data enthusiast with strong business sense and an analytical mindset: you proactively identify opportunities, build conviction, and align stakeholders to drive execution. And you care about the customers behind the data. You sit with users, understand how they consume data and where they get stuck, and turn that into product direction.

You'll join our Platform group and lead the strategy, roadmap, and execution of Hyro's analytics and insights platform. This core system turns data into actionable insight for our customers, powering customer-facing reports, our insight engine, and the underlying data infrastructure.

This is a senior IC role with end-to-end ownership over a critical, highly strategic product area. You'll work at the intersection of data, business, and engineering, translating a deep understanding of customer needs, business goals, and the technical aspects of the platform into an analytics strategy that drives growth.

Responsibilities
Define and drive the strategy, roadmap, and prioritization for our analytics product.
Own the end-to-end experience of our analytics product - including customer-facing reports and our insight engine, the primary way customers and internal stakeholders consume data-driven insights.
Partner closely with engineering and data teams to build and evolve the data infrastructure that powers reporting and insight generation.
Translate customer, business, and market insights into clear product direction and high-impact initiatives.
Define success metrics, track performance, and use data to inform decisions and communicate impact.
Partner with and present to senior leadership and C-level stakeholders, translating data into business strategy and growth opportunities.

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.