לוח משרות דרושים Data Scientist, לחיפוש זה נמצאו 84 משרות

We are seeking a highly motivated and talented Junior AI Scientist to join our FILM (Foundation Intelligence & Learning Models) team. This role is designed for an early-career professional or a recent graduate who is passionate about driving customer impact, through the frontier of Artificial Intelligence, Generative AI and agentic architecture. As a Junior AI Scientist, you will work closely with senior researchers and engineers to develop, refine, and deploy cutting-edge AI solutions that impact our core technology stack.
Responsibilities:
Assist in the research and development of Generative AI (GenAI) applications and frameworks.
Clean, analyze, and interpret large datasets to derive actionable insights for model training and evaluation.
Participate in the full lifecycle of GenAI development, from initial concept to deployment and monitoring.
Stay up-to-date with the latest advancements in GenAI/ML research and propose innovative ideas to solve complex problems.

We are looking for a visionary Principal Data Scientist to lead strategic research initiatives focusing on our Personal Loans asset class. In this high-impact, individual contributor role, you will be at the absolute forefront of our research department. You will tackle most challenging, long-term research questions, working hands-on to advance our sophisticated models while providing high-level professional leadership and mentorship to data scientists across the group.
Responsibilities:
Act as the technical authority and professional lead for the Personal Loans research group, setting the standard for scientific rigor and innovation.
Spearhead complex, long-term research initiatives that address fundamental challenges in credit risk, behavioral modeling, and portfolio optimization.
Develop cutting-edge Machine Learning models and algorithms from scratch, moving from high-level hypothesis to production-ready solutions.
Design multi-domain Data Science solutions for the companys core mission, identifying untapped opportunities within our vast datasets.
Mentor senior and staff data scientists, advising on methodology, experimental design, and technical roadblocks without the overhead of people management.
Partner with Product, Engineering, and Finance stakeholders to align long-term research goals with strategic business roadmap.

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!

We are an industry-leading technology company based in Tel Aviv, we are seeking a skilled Data Scientist to join our dynamic team. As a Data Scientist specializing in image analysis and text reading, you will play a pivotal role in developing and enhancing our AI-driven solutions for quick and accurate component inspection in electronic manufacturing.

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 a AI Solution Engineer.
The AI Solutions Engineer plays a critical, high-impact role in scaling our clinical AI solutions and ensuring the ability to deploy high quality products, monitor and support them in production.
This position requires strong software and data engineering skills to elevate our operational systems.
You will be responsible for independently driving complex E2E projects, demonstrating flexible thinking and making cost-effective decisions while balancing quality versus speed.
You will serve as a vital technical bridge, forming strong collaborations with both the R&D and Product organizations. Success in this dynamic and fast-driven environment demands a focused "getting things done" and "can-do" attitude to deliver value with speed and quality.
Responsibilities:
Automate and streamline AI operations - develop tools to automate AI deployment, data validation, and reporting for smooth production integration of AI solutions built by our research teams.
Act as the production technical expert - collaborate with Product and R&D teams during feature design to ensure production feasibility, scalability, and operational excellence.
Lead end-to-end tool development - gather requirements, design, implement, and deploy operational tools and pipelines in collaboration with cross-functional teams.
Drive engineering quality and play a key role in team member mentoring - uphold and develop engineering standards while fostering innovation.
Investigate and resolve complex data challenges - analyze and address production data issues, including data quality, availability, performance anomalies, and integration challenges.
Collaborate with customer-facing teams - understand real-world operational challenges, develop scalable solutions, and bridge the gap between customer needs and AI system behavior.
Ensure ongoing AI model reliability - design and implement mechanisms for monitoring data and performance degradation, and maintaining long-term model effectiveness.
Develop monitoring and analytics frameworks - create metrics and dashboards to track production health, data integrity, and AI performance across the deployment lifecycle.

we are looking for a Product Analyst Team Lead.

We're looking for a Data Scientist to turn our network data into a competitive advantage. You'll work directly with terabytes of telecom signaling, session, and usage data to build models that improve network selection, detect anomalies, optimize cost-per-gigabyte, and surface insights that shape product and commercial strategy. This is a high-impact role sitting at the intersection of ML, telecom engineering, and business decision-making.

What You'll Do

Design and deploy machine learning models on network data - including network selection optimization, churn and usage prediction, fraud and anomaly detection, and QoS/QoE forecasting.

Analyze CDRs, signaling events (Diameter, GTP, SIP), session logs, and radio-level metrics to identify performance issues, cost drivers, and growth opportunities.

Partner with the Network Operations and Product teams to translate raw telemetry into actionable signals - e.g., which carrier to steer traffic to in a given country at a given hour.

Build forecasting and segmentation models that inform pricing, capacity planning, and customer lifecycle strategy.

Own analyses end-to-end: framing the business question, exploring the data, building the model, validating it, and communicating findings to both technical and executive audiences.

Define and track KPIs for network quality, customer experience, and commercial performance, and build dashboards that make those metrics visible across the company.

Mentor junior data scientists and analysts, and help raise the bar on data science practices, code quality, and experimentation rigor.

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

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 a Senior Data Scientist to join the team that builds the predictive intelligence powering the app. You will own the ML models behind user engagement, cardiovascular risk stratification, and personalized health recommendations - the systems that determine what users see, when they're nudged, and how their health trajectories are shaped.

This role demands both statistical depth and engineering proficiency - you will be expected to take models from research through to deployment, write code built for production, and use AI coding assistants fluently as part of how you get work done.

Responsibilities
Lead end-to-end development of predictive ML models. From data exploration and feature engineering through training, validation, deployment, and ongoing monitoring across engagement and clinical risk domains.
Apply strong statistical foundations to model design, feature selection, uncertainty quantification, and interpretation of results
Write production-grade Python code that is clean, tested, and built for maintainability and scale.
Use AI coding assistants to accelerate development, code review, and documentation without sacrificing quality or rigor.
Partner with product managers, data engineers, and software engineers to translate strategic questions and user behavior patterns into measurable, data-driven solutions.
Research and implement cutting-edge ML techniques spanning supervised and unsupervised learning, causal inference, deep learning, and reinforcement learning to tackle complex healthcare challenges.
Contribute to MLOps infrastructure: model serving, versioning, evaluation pipelines, and monitoring.
Design and interpret A/B tests and other experimental methodologies to measure the impact of models, features, and interventions.

Were looking for a Data scientist Expert to join us and spread our power. As a Data Scientist you will be responsible for driving research and development of autonomous AI agents and LLM-powered systems. 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.
WHAT YOULL DO
Lead applied research on AI agents and LLM-driven features in the 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 are looking for a Fraud Data Scientist.
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.

we are looking for a Junior Fraud Data Scientist.
As a Junior Fraud Data Scientist, you will contribute to our ongoing efforts to protect our ecosystem from financial threats and abuse. Working under the guidance of senior team members in a data-rich environment, you will assist in identifying malicious behaviors, support detection coverage, and help maintain our automated mitigation strategies.
This role is designed for an analytical professional with 1-2 years of experience who wants to build a deep expertise in adversarial data, learn from an experienced team, and develop their technical skills across modern cloud data platforms.
What You Will Be Doing
Exploratory Data Analysis: Support the team by mining behavioral and transactional datasets to help identify anomalies and emerging fraud patterns.
Model Support & Optimization: Assist in building, tuning, and validating machine learning models (e.g., XGBoost, LightGBM) under the supervision of senior data scientists.
Feature Generation: Extract, engineer, and prepare new data features from structured and unstructured sources to help improve model performance.
Dashboarding & Monitoring: Build and maintain internal dashboards and pipelines to track model health, data drift, and key fraud KPIs.
Cross-functional Collaboration: Work alongside Fraud Analytics and Product teams to help translate operational fraud insights into automated data solutions.