לוח משרות דרושים Data Scientist בתל אביב יפו, לחיפוש זה נמצאו 49 משרות

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 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.

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.

We're looking for a Data Scientist (extended maternity leave cover) to push our ML capabilities further into the platform. Models are already running in production - your job is to make them sharper, keep them honest as the data shifts, and find the next opportunities worth building. This isn't a research-only role. It's an ownership role, end to end.

In this role, you will:
New ML opportunities get identified and proven out before engineering time is spent on them, because you research, prototype, and validate the model first.
Complex ideas land clearly across teams, because you can explain a model's logic and tradeoffs to engineers, product, and stakeholders without losing the substance.
Models keep working after they ship, because you own the full lifecycle: development, production deployment, drift monitoring, and retraining as the data changes.

we are looking for a Algorithm Team Lead
As a Team Leader on the Bidding Strategies Algo team in our TLV Office, youll play a vital role in shaping how Advertisers bid in real-time auctions at massive scale.
You will lead the optimization of our existing bidding strategies and the design of new ones, formulating bidding problems mathematically, applying control-theoretic methods, and analyzing the complex dynamics of a closed-loop.
By guiding team members and partnering closely with engineering, youll turn theoretical insights into robust production solutions. Your work will directly influence advertiser ROI efficiency across thousands of advertisers.

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 a Research Scientist - Sovereign AI Research.
We are hiring across the following specializations. We expect depth in at least one area and the intellectual range to collaborate across them.
Computer Vision: object detection, segmentation, multimodal grounding, vision-language models, contrastive and self-supervised representation learning, low-resource and few-shot visual recognition.
NLP / Speech: LLMs, NERs, relation extraction, span-based and generative IE, semantic textual similarity, multilingual and cross-lingual transfer.
Reinforcement Learning: MDPs, POMDPs, model-based and model-free RL, Online Offline methods, reward modeling, sim-to-real transfer, compute-aware planning.
Graph Learning: GNNs, graph clustering, community structure, generative methods, knowledge graph embeddings, dense and sparse semantic retrieval.
Optimization: convex and nonconvex optimization, constrained and Lagrangian methods, combinatorial and integer programming, knowledge distillation (response, feature, and relation-based), test-time optimization, Bayesian optimization, resource-aware inference.
Representation Learning: contrastive learning, self-supervised and unsupervised pre-training, disentangled representations, metric learning and embedding spaces, cross-modal and multimodal alignment, meta learning (hypernetworks), transfer learning and domain adaptation, probing and interpretability of learned representations, world models.
Neurosymbolic AI: neuro-symbolic integration, differentiable theorem proving, inductive logic programming (ILP), probabilistic soft logic (PSL), causal inference and structural causal models (SCMs), programmatic and compositional reasoning
Responsibilities:
Define and execute a research within your track, experiments, quality gates, and upper bounds in rigorous manner.
Collaborate across tracks on system integration and cross-disciplinary research.
Collaborate work with engineering teams until production.
Mentor junior researchers and contribute to a culture of technical excellence

we are looking for a AI Applied Scientist - Insights & Recommendations.
As AI Applied Scientist, youll be at the intersection of advanced technology and meaningful impact with clear business cases. You will have access to real-time (RT) big data from multiple Sensors & Data sources, and a chance to discover, explore research, and develop cutting-edge models that directly impact industrial manufacturers around the globe. As part of the Applied AI/ML Science team , you will have the opportunity to continuously grow and learn while building and deploying advanced models directly into our products, and stay at the forefront of the world's technologies, witnessing firsthand their impact on our customers.
A Day In Your Life:
Own the algorithm lifecycle from problem definition and data analysis to prototyping and delivering production-ready models.
Combine classic methods with inference techniques: statistics, Time series, Deep learning, anomaly detection, Recommendation Systems, Transformers, and Inference to extract data-driven insights.
Research, design, and build Agentic applications on top of sensor time-series data, textual data, images, ML applications, and other various data sources.
Engage with customers and collaborate with our product team to develop innovative solutions utilizing new types of data.
Leverage modern technologies: work with cloud-based big data platforms for storage, distributed processing, LLMs and agents (GPT, Claude, Gemini), defining & building Gurdrails, reasoning chain as function call, planning, ML-based vectorization and embeddings, and stream analysis.