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 an experienced Data Product Manager with a strong Data Science and Analytics background to own and lead data driven solutions.
In this role, you will define and drive the vision, strategy, and execution of data and analytics products that support our Marketing, Growth, and Acquisition teams. You will operate at the intersection of business, analytics, data science, and AI, translating marketing challenges and business questions into scalable data products, clear KPIs, and measurement frameworks that improve acquisition efficiency, ROI, and long-term revenue growth.
You will lead cross-departmental initiatives, partner closely with Marketing leaders, Data Scientists, Data Engineers, Analytics teams, and Product Managers, and ensure that advanced modeling, analytics, and AI capabilities are deeply embedded into day-to-day marketing decision-making.
Key Responsibilities
Marketing Data Product Ownership
Own the vision, roadmap, and delivery of Marketing Data Science and Analytics products
Align initiatives with business goals such as acquisition efficiency, ROI optimization, revenue growth, and funnel performance.
Act as the single point of ownership for marketing-related data, analytics, and data science products across teams.
Define data requirements, metrics, and success criteria for new initiatives, including identifying gaps in existing data.
Collaborate with Data Engineers and Analytics Engineers to design scalable data pipelines, data models, and semantic layers that support analytics, experimentation, and advanced modeling.
Prioritize initiatives based on business impact, scalability, data maturity, and clarity of success metrics.
Business Analytics, Data Science & AI-Driven Marketing Optimization
Define and own the business logic behind marketing KPIs, ensuring clear, consistent, and trusted metrics across teams.
Work closely with stakeholders to understand what needs to be measured, why and how.
Lead data science, analytics, and AI initiatives that optimize marketing performance
Own and evolve core marketing models including attribution, MMM, LTV prediction.
Define success metrics and measurement frameworks for models, experiments, and AI-driven decisions.
Drive experimentation frameworks (A/B tests, geo-experiments, uplift modeling) to continuously improve performance and decision quality.
Design and deploy AI-powered and agentic solutions to automate and scale marketing workflows such as budget allocation, campaign optimization, and creative testing.
Ensure solutions are production-ready, measurable, explainable, and actionable for business stakeholders.
Translate complex model outputs into clear, trusted insights for business users.

We are looking for an experienced and strategic Product Manager to lead a core domain within our companys Cloud Firewall portfolio. This role is central to shaping how enterprises secure cloud environments at scale across hybrid and multi-cloud infrastructures.
our companys cloud security solutions protect some of the worlds largest organizations, and our Cloud Firewall capabilities are a critical part of helping customers enforce consistent, scalable, and intelligent network security in dynamic cloud environments.
As the Product Manager for a core Cloud Firewall capability, you will own the product vision, strategy, and execution end to end - from deeply understanding customer needs, market trends, and cloud platform evolution, through defining roadmap priorities and driving delivery, to ensuring strong adoption and long-term customer value.
You will work closely with engineering, architecture, UX, research, sales, marketing, and customer-facing teams in a highly cross-functional environment. This role requires a strong product leader with proven success in Agile product development, the ability to lead complex cross-functional programs, and a demonstrated record of influencing strategy and execution across engineering, business, and customer-facing teams.
This is an opportunity to influence a strategic cloud security domain and build high-impact products that solve real customer problems in one of the most important areas of cybersecurity.
Key Responsibilities
Define and own the product strategy, vision, and roadmap for our companys Cloud Firewall capabilities, aligned with company objectives, customer needs, and market opportunities.
Lead product discovery by analyzing customer pain points, industry trends, competitor offerings, and emerging cloud security requirements across AWS, Azure, GCP, and hybrid environments.
Work closely with engineering leadership and development teams to translate strategy into execution through clear priorities, well-defined requirements, and pragmatic tradeoff decisions.
Drive product development in a proven Agile environment, including backlog management, feature prioritization, user story definition, sprint planning support, and ongoing collaboration with R&D throughout the delivery lifecycle.

We are looking for an exceptional Senior Product Manager to join our AI Security group and play a key role in shaping the future of AI Security As a Product Manager, you will drive product strategy, define innovative security capabilities, and work closely with engineering, security researchers, and customers to build solutions that help organizations securely embrace the AI era.

Responsibilities:
Identify emerging challenges, opportunities, and customer requirements related to AI Security, AI applications, AI agents, and data protection.
Define and drive product strategy for AI Security initiatives across platform.
Break down strategic product goals into actionable plans and lead execution across engineering, research, design, and go-to-market teams.
Define product requirements and oversee the implementation of new AI Security capabilities and services.
Conduct market research, competitive analysis, and customer discovery to understand evolving AI Security threats, trends, and customer needs.
Work closely with customers, partners, and internal stakeholders to validate problems, prioritize opportunities, and shape product direction.
Analyze product performance, user behavior, and customer feedback to continuously improve product effectiveness and business impact.
Collaborate closely with world-class engineering and security teams to bring innovative AI Security solutions to market.
Drive cross-functional initiatives involving Product, R&D, Security Research, Sales, Marketing, and executive leadership.

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.

As a Senior Product Manager at our company, you will sit at the intersection of data science, engineering, and product, translating complex ML and causal inference capabilities into a product our customers love. You will work hand-in-hand with our data science and the engineering teams to shape how models are designed, evaluated, and delivered into production.
Own a significant part of the product roadmap for our value measurement platform, with a focus on data science driven features (causal inference, incrementality modeling, attribution, and media mix optimization) and customer onboarding.
Partner closely with data scientists from problem framing through experimentation, model validation, and productization, youll be expected to critically read model specs, challenge assumptions, and help prioritize research directions based on customer impact
Define success metrics and evaluation frameworks for ML models alongside the DS team, balancing statistical rigor with business usability
Own the UX of data-heavy features: collaborate with designers and data scientists to turn complex model outputs into intuitive user flows, clear visualizations, and dashboards that customers can actually act on
Design and prioritize dashboards, reports, and in-product analytics, defining what metrics to surface, how to visualize uncertainty and causal results, and how to make insights discoverable without overwhelming the user
Run user research and customer interviews to validate both the analytical value and the usability of features, and onboarding integration; translate findings into concrete design and product improvements
Translate customer problems into well-scoped technical and design requirements; translate model outputs and methodology into clear value propositions for customers
Prepare the implementation of features and support engineering, design, and DS teams through design reviews, QA, and launch, including writing acceptance criteria that account for model behavior, edge cases, data quality, and UX states (empty, loading, partial data, error)
Ensure data integrity through testing, acceptance tests, and ongoing monitoring across our diverse range of data products
Work with clients and internal stakeholders to understand business goals, identify relevant KPIs, and feed those insights back into the product, modeling, and dashboarding roadmap
Oversee end-to-end data flow and engineering processes, collaborating with ML, data, and engineering teams to ensure efficient and accurate operation during critical phases like client onboarding and daily production runs
Strategically plan for missing or imperfect data, designing product behavior and UX for new client onboarding, process failures, client-specific configurations, long-term seasonality, and external signals/features
Drive experimentation: help design A/B tests, back-tests, and validation studies to assess the impact of new models, features, and UX changes.

1. AI Architecture & Technical Leadership

Guide AI architectural direction across platform, focusing on system design, model lifecycle, and integration of AI components into product workflows.
Act as a senior technical reviewer and thought partner for complex or cross-team AI design decisions.
Provide technical oversight on areas such as exploring new models/technologies/opportunities for the company
Surface architectural risks, tradeoffs, and long-term implications, including cost, security and compliance aspects and clearly advise the VP of AI when certain technical directions should not be pursued.
This role influences judgment, clarity, and experience, not through blocking authority.

2. Hands-on Applied Innovation (Core Pillar | ~50%)

Spend at least 50% of time hands-on, building:
End-to-end AI prototypes
Technical demos and proofs of concept
Exploratory implementations of new AI capabilities
Drive applied innovation that:

De-risks new technologies
Demonstrates feasibility and impact
Informs product direction and business opportunities
Build fast, concrete examples that teams can learn from and extend.
Transition successful prototypes to team ownership for further development and scaling.
This role is expected to lead AI innovation by doing, while working closely with product, medical and engineering

3. Best Practices & Technical Enablement

Define and promote best practices for applied AI development, including:
Rapid prototyping and vibe coding.
Agent design, orchestration, and evaluation patterns
Experimentation, benchmarking, and validation workflows
Help teams align on shared technical patterns, tools, and standards.
Identify opportunities to consolidate duplicated efforts and improve cross-team coherence.
Lead technical deep dives, architecture discussions, and design reviews.
4. AI Compliance & Regulatory Enablement (Technical Scope)

Ensure Navinas AI development practices align with applicable AI regulations for a software product handling sensitive medical data.
Define and guide AI-specific compliance practices, including data usage, transparency, evaluation, and documentation expectations.
Support and contribute to AI-related compliance and regulatory documentation, in close collaboration with Legal, Security, and Medical Research teams.
Serve as a technical point of reference for AI compliance questions.

What you'll be doing
Drive the detection product domain: Bring deep expertise in SecOps engineering across SIEM, data pipelines, and SOAR, and help the product team define where the product should excel. Develop a strong understanding of the day-to-day work of SecOps engineers and the challenges they face.
Turn expertise into product insight: Conduct deep, hands-on research across real security environments, SecOps engineering practices, data, and tooling. Combine customer feedback with your own experience and the knowledge of the broader team to identify meaningful problems, opportunities, and product directions.
Shape and execute the roadmap: Translate product insights into a focused roadmap aligned with company strategy and customer needs. Partner with the product and engineering teams to prioritize initiatives, make tradeoffs, define outcomes, and drive execution from discovery through delivery.
Turn problems into product direction: Translate ambiguous problems into clear product requirements and actionable plans. Work closely with engineering, design, and security experts to ensure complex technical problems are translated into products that are useful, intuitive, and technically sound.
Shape and evaluate AI agent capabilities: Define what our companys AI agent should be capable of doing as a SecOps engineer, what good looks like, and where it must be trusted or constrained. Develop evaluation frameworks, scenarios, and ground truth to measure and continuously improve agent performance.
Own product quality and outcomes: Maintain a deep understanding of how the product is used in real-world security operations. Validate assumptions, identify gaps, and use customer feedback, product data, and operational experience to continuously improve the product and its direction.

We are looking for a Senior Product Manager to join our Agentic Search team, building the search and retrieval infrastructure that powers AI agents and LLM-based applications at scale.

This is a hands-on IC role with real ownership. You will take full end-to-end responsibility over a core product area, from defining strategy through shipping and iterating based on real usage data. You will work within a small PM team reporting to the Director of Product, alongside engineering, design, and go-to-market, shaping how AI agents discover, retrieve, and act on web information in real time.

The product serves both a developer-facing PLG motion and a growing enterprise customer base, so you will need to be equally comfortable digging into API design and search quality as you are running customer discovery calls and informing competitive positioning. We are looking for someone who can operate as a senior strategic partner, not just an executor, and who has the potential to grow into a principal-level PM over time.

Youre welcome to work hybrid from Tel Aviv, Israel.

Your responsibilities will include:

Own a core product area end-to-end: strategy, roadmap, execution, and iteration based on real usage signals and customer feedback.
Drive search quality and relevance improvements by working closely with engineering and research to define evaluation frameworks, benchmarks, and release criteria.
Engage directly with enterprise customers and developer users to validate ideas, understand pain points, and shape product-market fit across both PLG and enterprise motions.
Monitor the competitive landscape in agentic search and AI-powered retrieval, identifying shifts, threats, and opportunities that should inform product decisions.
Collaborate with go-to-market teams (sales, solutions, growth) to ensure product positioning, ICP definitions, and enablement materials reflect product reality.
Contribute to product strategy as a senior voice on a small, high-impact team, challenging assumptions and bringing independent judgment to key decisions.
Lead product experiments and rapid iteration cycles, moving from hypothesis to prototype to production with clear success criteria.

We are looking for an experienced Senior Product Manager to own Torq's Builder Experience domain - a core part of how security teams design, build, and deploy automations. As AI fundamentally changes how security operations work, this role sits at the center of that shift.

This role requires genuine product craft - the ability to see what security teams actually struggle with and shape tools that feel intuitive and powerful - alongside frontier AI product thinking: defining how a conversational AI should behave, where its limits are, how it earns trust, and how it sharpens with every interaction.

What Youll Be Doing

Own the end-to-end builder experience - from understanding where security teams get stuck to shipping flows that feel intuitive and powerful
Care about how the product looks and feels. Partner closely with Design to turn ambiguity into clear, usable flows
Drive our conversational AI product forward as a first-class part of the platform
Own how we evaluate the AI experience: define what "good" means, how we measure quality, and how we improve it continuously
Perform customer research and hands-on deep-dives to understand how people build and where they get stuck
Analyze usage patterns and feedback to spot signal and translate it into actionable engineering requirements
Make critical decisions on trade-offs across experience, capability, and technical design to meet business objectives
Work closely with engineering to define, develop, and launch new capabilities