לוח משרות דרושים Data Scientist, לחיפוש זה נמצאו 82 משרות

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 are looking for a hands-on Senior Applied AI Evaluation Engineer to improve how our RAN R&D organization analyzes engineering tickets, supports Root-Cause Analysis, recommends ownership, and learns from resolved cases.
You will build rigorous evaluation datasets, establish meaningful baselines, compare internal and approved external tools, analyze failure modes, and prototype improvements across retrieval, classification, prompting, agent workflows, and model selection.
This role is focused on measurable, evidence-based improvement rather than AI demonstrations. You will assess whether AI-generated conclusions are accurate, grounded in evidence, appropriately calibrated, and useful to engineering teams.
As a secondary area of focus, you will analyze engineering workflows at case and team level to identify bottlenecks, handoffs, dependencies, and opportunities for process improvement.

we are looking for a hands-on technical leader to build and operate a secure local large-language-model platform for the company. The platform will allow engineering and business teams to use generative AI with proprietary source code, product documentation, technical standards, test artifacts, support knowledge, and other approved internal data while keeping sensitive information within company-controlled environments.
This is a senior individual-contributor role spanning applied LLM engineering, platform architecture, search and data pipelines, security, and production operations. You will turn promising prototypes into a dependable internal capability: selecting and optimizing open-weight models, building permission-aware retrieval, creating reusable APIs and tools, integrating with existing engineering workflows, and establishing objective ways to measure quality, safety, latency, capacity, and business value.
The successful candidate will understand that a useful enterprise LLM is more than a model and a chat interface. It requires trustworthy source grounding, strong access controls, repeatable evaluation, careful tool permissions, observable production services, and an operating model that keeps data, indexes, prompts, models, and dependencies current. You will make pragmatic build-versus-buy decisions and choose the simplest approach-search, retrieval-augmented generation (RAG), prompting, workflow automation, or model adaptation-that meets each use case.
Initial use cases may include engineering knowledge discovery, source-code understanding, troubleshooting assistance, technical-document Q&A and summarization, test and log analysis, and drafting structured engineering artifacts. The platform should be extensible to additional approved use cases as needs and model capabilities evolve.

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.

Required Staff AI Research Scientist
Our research is driving the future of AI, from edge to cloud. We are building the foundations of tomorrow's intelligent systems, spanning next-generation processors and accelerators, innovative machine learning models, and advanced AI tools. As a Research Scientist, you will join a distributed team pushing the boundaries of what is possible at the intersection of AI, computer architecture, and compute. We seek curious, collaborative researchers who are passionate about turning research ideas into top-tier publications and real-world impact. We are looking for Research Scientists interested in foundational advances in machine learning, efficient inference, and physical AI. Research areas can include, but are not limited to:
Generative Modeling: Research into generative techniques for inverse problems, such as image denoising, super-resolution, reconstruction, and related tasks.
Efficient Inference: Research into including quantization, pruning, low-rank, and hardware-aware optimization.
Physical AI: Research into AI systems that perceive, reason, and act in the physical world.
Responsibilities:
This role involves conducting research across a wide range of AI topics, collaborating with researchers to advance the state of the art, publishing at top conferences, and translating research into real-world impact.

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're looking for a Senior Applied Researcher and Team Lead to help shape how our conversational agents are designed, evaluated, and shipped reliably at production scale.



Key Responsibilities

Partner directly with the Applied Research Team Lead to define and evolve team process, technical standards, and the quality bar for agent behavior.
Take real user experience and data through the full arc, from concept to a reliable agent design to a shipped, production-grade product.
Own or share ownership of the standing function of assessing AI tools, models, and providers for reliability and fit.
Raise the technical bar across a team spanning research-leaning grad students to engineers newer to AI, and turn personal expertise into shared tooling and standards the whole team can use.
Operate pragmatically inside current infrastructure constraints while flagging where structural fixes are needed.

A growing B2B SaaS company is looking for an experienced Head of Data Science to lead its Data Science function and drive the next generation of AI and machine learning capabilities.

This is a strategic leadership role combining people management, hands-on problem-solving, cross-functional collaboration, and ownership of data-driven innovation across the organization.

Responsibilities

Lead and mentor a global team of Data Scientists.

Define and execute the company's Data Science strategy.

Drive the development of advanced machine learning, optimization, and forecasting solutions.

Partner closely with Product, Engineering, Customer Success, and executive leadership.

Translate complex business challenges into scalable analytical solutions.

Support customer-facing initiatives and strategic engagements when needed.

Establish best practices for experimentation, model evaluation, and performance measurement.

Act as a key stakeholder in shaping the company's AI vision and roadmap.

we are looking for a Customer-Facing Data Scientist.
As a key Customer-Facing Data Scientist on the Demand Forecasting team, you will:
Work at the forefront of the data world, focusing on the optimization and deployment of Demand Forecasting models.
Handle complex data science challenges using state-of-the-art techniques relevant to demand forecasting and time series, such as forecasting model ensembling, anomaly detection, feature engineering for trend/seasonality, and causal inference modeling.
Act as a highly communicative partner to our clients, diving deep into their unique datasets to understand the nuances and challenges of their business.
Lead the rigorous testing and application of our core ML pipeline on new customer data, identifying areas where it performs exceptionally well and researching/developing solutions for complex cases where it does not.
Be instrumental in helping our clients achieve their business goals and realize the measurable value of Demand Forecasting module.
Work within a production and product-oriented environment, ensuring models are robust, scalable, and directly integrated into the clients operational workflow.

Were looking for an experienced Product Analytics Manager to play a key role as part of the Product organization. The Product Analytics Manager will lead the domain and work closely with Product, R&D, Sales and Marketing, Customer Success, and BI to drive data-informed product and business decisions.
Key Responsibilities
Define product success metrics, usage benchmarks, and adoption KPIs across products and customer segments, ensuring alignment with clinical and business impact goals.
Own the end-to-end product analytics framework - including event design, tracking strategy, data validation, and ongoing data quality monitoring.
Perform hands-on deep-dive analyses, working directly with raw data and collaborating with R&D and BI to ensure robust data models, pipelines, and instrumentation.
Partner with Customer Success and BI to deliver customer-centric insights and support the evolution of the product data infrastructure.
Build dashboards and analytical models to support product and business decision-making, evaluate feature performance, and establish a continuous feedback loop between user behavior and product development - using data-driven insights to challenge assumptions and prioritize high-impact roadmap initiatives.
Quantify the clinical and economic impact of solutions by analyzing real-world encounter data to validate key performance markers such as reduction in cost of care, improved patient outcomes, and product utilization.
Translate complex analyses into clear, actionable recommendations for technical and non-technical stakeholders.
Provide professional leadership in analytics, including team management (if applicable) and contributing to the growth of the analytics function.