לוח משרות דרושים הייטק דאטה בפתח תקווה

As a Senior Product Analyst at our company, youll join the AI Solutions group, contributing to the development of intelligent identity security capabilities. This role sits at the intersection of data, product, and AI - giving you the opportunity to apply analytical thinking and technical skills to help shape real-world solutions. Youll work alongside senior analysts, data scientists, engineers, and product managers to explore data, derive insights, and contribute to productizing AI-driven features that improve the performance, usability, and intelligence of our offerings.
Analyze product usage and behavioral data to support the design and improvement of AI-powered features.
Work closely with product managers and engineers to translate insights into productized solutions that enhance functionality and user experience.
Build and maintain dashboards, KPIs, and reports that drive product decision-making.
Contribute to the development and validation of success metrics for new capabilities.
Collaborate with data scientists on experiments, A/B tests, and model monitoring efforts.
Ensure data quality and support scalable pipelines by working with data engineering teams.
Communicate findings through clear presentations and visualizations that influence roadmap decisions.
Develop a deep understanding of product workflows and user behavior to guide analytical priorities.

We are seeking a Data Architect to lead the design of end-to-end, enterprise data architectures that enable analytics, AI, and data-driven transformation across customer environments.
In this role, you will act as a trusted advisor, defining the architectural vision, target-state designs, and roadmaps for IBM data solutions. You will work closely with customers, sales manager, and presale engineers to translate business needs into scalable, secure, and future-ready data platforms across hybrid and multi-cloud environments.
Responsibilities:
Define data architecture vision, target-state, and roadmaps for customer environments
Design enterprise-scale architectures for data warehouses, data lakes, lakehouse, and streaming platforms
Architect data platforms to support analytics, AI, and generative AI use cases
Act as a trusted advisor to customers on data modernization, governance, and AI readiness
Ensure architectures align with security, governance, and regulatory requirements
Guide implementation teams to ensure adherence to architectural standards
Collaborate with Consulting, Sales, and Delivery teams on solution design and proposals
Contribute to IBM reference architectures, standards, and reusable patterns

We are seeking a visionary and hands-on AI Scientist to help shape the next generation of foundational language models and agentic systems that empower small and medium-sized businesses (SMBs) to thrive. In this role, you will play a key part in defining, designing, and building groundbreaking AI solutions that understand, reason, and act across complex financial and business contexts.
You will collaborate closely with cross-functional experts in AI research, data science, engineering, and product to create intelligent agents that help SMBs automate financial tasks, make smarter decisions, and operate more efficiently. Your work will combine scientific rigor with visionary execution, balancing theoretical innovation with large-scale practical impact.
The ideal candidate is a builder-scientist-a creative thinker who not only develops novel model architectures and training paradigms but also drives their implementation and validation in real-world applications. Youll have the opportunity to shape the AI foundation that powers transformative products serving millions of customers.
Responsibilities:
Design and develop foundational language models and agentic architectures that power intelligent decision-making for small and medium businesses.
Explore novel approaches in large-scale pretraining, fine-tuning, retrieval-augmented generation, and reasoning to improve factuality, adaptability, and alignment.
Build intelligent agents capable of understanding financial data, customer context, and business workflows - from bookkeeping and planning to communication and insights generation.
Collaborate with cross-functional teams across AI research, engineering, product, and design to translate breakthrough science into scalable products used by millions.
Define technical strategy and roadmap for next-generation agentic systems, driving innovation and excellence across the organization.
Publish and present research findings, internal innovations, or contributions to open-source frameworks where appropriate.

As a Senior Analyst on the Trust and Safety Analytics team, you will play a critical role in identifying, analyzing, and operationalizing key data signals to help prevent fraud, protect customers, and enforce acceptable use of products and services. You will apply strong analytical and technical skills to support and scale risk detection tools, monitoring processes, and mitigation strategies across Trust & Safety.
This role partners closely with Product, Engineering, and Operations teams to deliver insights that drive data-informed decisions and measurable risk reduction.
Responsibilities:
Partner with Engineering and Product teams to support the design, implementation, and maintenance of analytics tools, dashboards, and data pipelines that enable effective fraud and risk monitoring.
Analyze large, complex datasets to identify trends, anomalies, and emerging fraud patterns, translating findings into actionable recommendations.
Contribute to the ongoing improvement of Trust & Safety platforms, including monitoring performance metrics, assessing platform health, and identifying risk gaps or degradation.
Design and maintain dashboards and reporting (e.g., Tableau) to track fraud performance, operational efficiency, and risk exposure.
Ensure data quality and reliability by performing validation, testing, and monitoring of analytics outputs; proactively identify and escalate issues.
Support incident analysis and investigations by using data to identify root causes, quantify impact, and recommend mitigation actions.
Stay current on fraud trends, AI-driven abuse, and industry best practices, incorporating new insights into analyses and detection strategies.
Clearly communicate insights to both technical and non-technical stakeholders, influencing decisions through data-backed storytelling.

We are looking for team members that love new challenges, cracking tough problems, and working cross-functionally.
You will be working end-to-end from ideation to implementation and have an opportunity to see your models make a significant impact on products and customers security.
Responsibilitie:
Develop ML models to identify security exploits, that based on a wide range of algorithms and techniques (classification, clustering, graph based)
Explore various data sources while leveraging your domain knowledge to craft creative features to boost model performance
Lead and innovate AI projects in the Security domain, lead discussions and understand the needs of the project from the business units and be able to integrate your domain knowledge into the algorithms you'll build and implement in production
Be an active partner of the full cycle of iterative big data exploration, including hypothesis formulation, algorithm development, data cleansing, testing, insight generation, and visualization

Join an amazing team driving AI solutions in the fintech industry, primarily focused on evaluating risk for lending products.
This Senior Staff role is a blend of leading the full AI/ML development cycle and acting as a Data Science Architect, responsible for all engineering aspects of the work to deliver excellent, high-quality, and operationally sound AI systems.
Responsibilities:
System Architecture & Operational Excellence:
Deliver robust AI/ML system designs that meet customer and systems requirements, including latency, throughput, cost, and maintenance.
Working with partners and architects, define the vision and align on architecture for integration, ensuring excellent deliveries in terms of quality, stability, security, compliance, and operational excellence.
Drive cross-functional end-to-end design architecture.
Lead conversations with partner architects to influence AI-driven product design and strategy
AI/ML Development & Modeling:
Lead the full cycle of iterative big data exploration, including hypothesis formulation, algorithm development, data cleansing, testing, and insight generation.
Apply advanced, predictive statistical modeling and testing to extract data-driven insights from complex, large-scale data sources (relational and non-relational).
Review complex and challenging work to help simplify and execute with speed.
Strategy & Consultation:
Provide guidance to business stakeholders on how best to harness available data and fully exploit the insights revealed through the research.
Drive the design and creation of reusable AI capabilities.

As a hybrid analyst-data scientist, youll partner closely with Customer Success, Professional Services, Training, and Support teams to deliver both advanced modelling, and foundational analyses. Youll uncover patterns, build models, and translate complete findings into clear, actionable recommendations that influence senior leadership decisions and improve Enterprise customer outcomes.
If you thrive on uncovering patterns in data, influencing high-level business strategy, and translating insights into measurable customer outcomes, this role is for you.
Responsibilities:
Work as part of a small Customer Success Insights and Operations team that leads data analysis, predictive insights and operational support to a growing CS team dedicated to the success of our customers.
Deliver actionable analyses and insights across key Enterprise CS OKRs, initiatives and growth experiments - leading 2-3 strategic projects per Quarter. These projects could be anything from churn analysis, expansion-likelihood modelling, behaviour clustering, to NPS sentiment and customer success plan performance.
Build, test and maintain predictive and statistical data models that identify risk, quantify revenue opportunities and prioritise CS actions at scale.
Support the creation of dashboards with our BI team that track customer health, renewal risk, and engagement impact across our enterprise accounts.
Design and analyse growth and expansion experiments to identify and scale opportunities for product adoption and usage expansion.
Partner with CS leadership to support MBRs/QBRs, operational reviews, and executive reporting.
Drive cross-functional collaboration with Product, Sales, and Marketing to ensure alignment on customer insights and opportunity areas.
Respond to specific internal CS requests to support customer retention and growth plays, delivering 5+ high-value analyses monthly that fuel CS OKRs and strategic decision-making.
Leverage statistical and analytical tools (e.g., SQL, Python, statistical / ML libraries as well as BI tools like Qlik) to model data, test hypotheses, and communicate recommendations with clarity and impact.
Explore and help operationalise the use of AI-tools like ChatGPT and Google Gemini to support analysis velocity, identify patterns and automate insights generation.
Champion data quality and governance, ensuring reliable and scalable data pipelines in partnership with MIS and Data Engineering teams.
Advise on KPI design and performance tracking for Enterprise CS metrics including NRR, GRR, churn, and adoption health scores.

Our team is responsible for the data and data infrastructure that processes billions of records daily, driving critical business insights for both internal and external customers across the organization.
Our Data team consists of highly skilled senior software and data professionals who collaborate to solve complex data challenges. We process billions of records daily from multiple sources using diverse infra and multi-stage pipelines with intricate data structures and advanced queries, and complex BI.
A bit about our infrastructure. Our main databases are Snowflake, Iceberg on AWS, and Trino. Spark on EMR processes the huge influx of data. Airflow does most of the ETL.
The data we deliver drives insights both for internal and external customers. Our internal customers use it routinely for decision-making across the organization, such enhancing our product offerings.
What Youll Do
Build, maintain, and optimize data infrastructure.
Contribute to the evolution of our AWS-based infrastructure.
Work with database technologies - Snowflake, Iceberg, Trino, Athena, and Glue.
Utilize Airflow, Spark, Kubernetes, ArgoCD and AWS.
Provide AI tools to ease data access for our customers.
Integrate external tools such as for anomaly detection or data sources ingestion.
Use AI to accelerate your development.
Assures the quality of the infra by employed QA automation methods.

We are seeking a Senior Data Scientist to be one of the first data science hires for our newly formed ML team. This is a unique ground-floor opportunity to build our data science capabilities from scratch and have direct influence on our analytical and modeling approaches. This role combines advanced data science expertise with foundational contributions, requiring someone who can develop sophisticated models, conduct deep analytical research, and help establish the data science methodologies that will guide the team as we scale. As one of our founding data scientists, you'll work closely with our Tech Lead to shape our modeling approaches, define our analytical standards, and build the fundamental data science skill set for future hires.
What You'll Do:Empty heading
Build foundational data science capabilities: As one of our first data science hires, work with the Tech Lead to develop core modeling methodologies, analytical frameworks, and research practices
End-to-end model development: Take ownership of complete ML model lifecycle - from initial research and experimentation through production deployment and monitoring
Build impactful models: Design, develop, and deploy machine learning models that solve complex business problems across payments, risk, and customer analytics
Define analytical standards: Help establish best practices for data exploration, feature engineering, model validation, and statistical analysis
Conduct deep research: Lead exploratory data analysis and research initiatives to uncover insights and identify ML opportunities across the business
Partner with stakeholders: Work closely with business teams to understand requirements, translate problems into data science solutions, and communicate findings effectively
Establish model development practices: Help set up our approach to model experimentation, A/B testing, and performance monitoring
Shape data science culture: Contribute to establishing the team's analytical rigor, documentation standards, and knowledge-sharing practices
Drive innovation: Research and evaluate new ML techniques, algorithms, and approaches that could benefit our business use cases.

We are seeking a seasoned and execution driven VP, head of data platform to lead all the R&D tracks of the Data and Analytics organization in our company. This role focuses on leading a complex data engineering organization at scale, driving delivery, operational excellence and cross-company enablement through world-class data platforms and tools.
This leader will serve as the engineering owner of our data platform, managing multiple data infra, data engineering and BI development teams, overseeing extensive resources, and ensuring the delivery of high-quality, scalable, and reliable data products and services. This position requires a strong technical background combined with exceptional leadership, strategic thinking, and cross-functional collaboration skills.
He will also act as a key stakeholder in defining the architecture and strategic direction of our data infrastructure, pipelines, AI/ML infra and platforms to support the company's rapid growth and evolving business needs.
Hybrid
Full-time
What youll do: 
Lead and manage the entire data platform, including:
Real-Time Data & Streaming Infrastructure: Introduce and engineer robust data streaming infrastructures (e.g., using Kafka, Pub/Sub, Dataflow) to enable near-real-time data ingestion and scalable low-latency data serving, unlocking advanced analytics and critical use cases.
Data engineering teams responsible for the data ingestion, transformation and delivery pipelines
BI infra and development teams owning the cross-company consumption layer, including BI tools, BI data layers and dashboards
Serve as the operational and delivery lead across the data platform, ensuring strong project execution, roadmap alignment and measurable business impact
Data Engineering & Architecture Oversight: Lead the design, development, and evolution of scalable data platforms, encompassing data lakes, data warehouses, and advanced data products, ensuring they meet performance, reliability, and business requirements.
Operational Excellence & Reliability: Establish and drive engineering operational excellence processes across data engineering, significantly improving data quality, availability, and system reliability. Implement frameworks for proactive monitoring, alerting, and incident management, reducing major incidents and ensuring continuous visibility into data flows.
Advanced Data Observability: Integrate and leverage cutting-edge data observability solutions (e.g., Monte Carlo) to provide comprehensive visibility into data pipelines, enabling proactive detection and resolution of anomalies.
Cross-functional Collaboration & Stakeholder Management: Collaborate extensively with product, analytics, business, and infrastructure teams to align data strategies with overarching business priorities, ensuring the delivery of high-quality data products that meet diverse user needs.
Innovation & Technology Adoption: Stay abreast of the latest data, cloud, and AI trends, driving the evaluation and adoption of modern cloud-native technologies to continuously improve platform capabilities and future-proof the ecosystem.
Leadership & Team Growth: Lead, mentor, and grow a large team of data engineers, fostering a culture of technical excellence, continuous learning, and agile methodologies. Oversee budget management and drive talent development within the team.
Data Governance & Quality: Oversee the implementation of standards for data quality, data integrity tools, governance, privacy, and regulatory compliance across the data ecosystem.

We are looking for a Senior Data Engineer to build and operate a multi-tenant analytics platform on AWS + Kubernetes (EKS), delivering streaming and batch pipelines via GitOps as a Platform-as-a-Service (PaaS).
Responsibilities:
Ingestion pipelines: Build and operate Flink / Spark streaming and batch jobs ingesting from Kafka, S3, APIs, and RDBMS into OpenSearch and other data stores.
Platform delivery: Provide reusable, multi-tenant pipelines as a self-service PaaS.
Workflow orchestration: Manage pipeline runs using Argo Workflows.
GitOps delivery: Deploy and operate pipelines via ArgoCD across environments.
IaC & AWS: Provision infrastructure with Terraform and secure access using IAM / IRSA.
Reliability: Own monitoring, stability, and troubleshooting of production pipelines.
Collaboration: Work with product, analytics, and infra on schemas and data contracts.

we are looking for a Senior Data Analyst to join our growing Analytics & Growth team and play a key role in tracking, analyzing, and optimizing the companys overall performance across customers.
The role focuses on defining event tracking, attributes, and properties mapping, analyzing conversion funnels, and identifying trends or issues using web analytics tools. You will work closely with R&D, Professional Services, Product managers, and the Business Intelligence team to ensure data integrity, validate tracking implementations, support new feature launches, and enable data-driven decision-making across the organization.
The ideal candidate is highly independent, curious, and proactive, with a strong drive to uncover root causes and continuously learn new tools and methods to solve complex analytical challenges
Define, implement, and maintain event tracking, attributes, and properties across web and product platforms
Gather and analyze data from multiple sources, including web analytics, internal systems, session recordings, and online browsing activities
Conduct in-depth analyses of customers conversion funnels and trends to identify optimization opportunities, as well as raise alerts and potential issues
Monitor KPIs related to conversion, engagement, and overall company performance
Guide and support our e-commerce funnel optimization with data backed insights.
Collaborate with R&D, product managers, and cross-functional teams to define tracking needs, raise alerts, and support new feature launches
Ensure web analytics data is accurately reflected in BI dashboards and reports, enabling reliable decision-making and insights
Work closely with the BI team on reporting requests, dashboards design, QA, and acceptance before release
Provide actionable, data-backed insights to support company-wide strategy and analytical projects
Take initiative to learn independently, adopt new analytical approaches, and work autonomously on complex problems.

Were looking for a highly skilled and motivated Data Engineer to join the Resolve (formerly DevOcean) team .
In this role, youll be responsible for designing, building, and optimizing the data infrastructure that powers our SaaS platform.
Youll play a key role in shaping a cost-efficient and scalable data architecture while building robust data pipelines that serve analytics, search, and reporting needs across the organization.
Youll work closely with our backend, product, and analytics teams to ensure our data layer remains fast, reliable, and future-proof. This is an opportunity to influence the evolution of our data strategy and help scale a cybersecurity platform that processes millions of findings across complex customer environments
Roles and Responsibilities:
Design, implement, and maintain data pipelines to support ingestion, transformation, and analytics workloads.
Collaborate with engineers to optimize MongoDB data models and identify opportunities for offloading workloads to analytical stores (ClickHouse, DuckDB, etc.).
Build scalable ETL/ELT workflows to consolidate and enrich data from multiple sources.
Develop data services and APIs that enable efficient querying and aggregation across large multi-tenant datasets.
Partner with backend and product teams to define data retention, indexing, and partitioning strategies to reduce cost and improve performance.
Ensure data quality, consistency, and observability through validation, monitoring, and automated testing.
Contribute to architectural discussions and help define the long-term data platform vision.

We are seeking a Senior Backend & Data Engineer to join its SaaS Data Platform team.
This role offers a unique opportunity to design and build large-scale, high-performance data platforms and backend services that power our cloud-based products.
You will own features end to end-from architecture and design through development and production deployment-while working closely with Data Science, Machine Learning, DevOps, and Product teams.
Key Responsibilities:
Design, develop, and maintain scalable, secure backend services and data platforms on AWS
Build and operate batch and streaming ETL/ELT pipelines using Spark, Glue, Athena, Iceberg, Lambda, and EKS
Develop backend components and data processing workflows in a cloud-native environment
Optimize performance, reliability, and observability of data pipelines and backend services
Collaborate with ML, backend, DevOps, and product teams to deliver data-driven solutions
Lead best practices in code quality, architecture, and technical excellence
Ensure security, compliance, and auditability using AWS best practices (IAM, encryption, auditing).