דרושים » תוכנה » Data Scientist - Match Quality & Feedback Loops

Were looking for a Senior Data Scientist to be a core driver in how our product empowers security teams. You will be expected to deeply understand customer needs and translate them directly into product features that deliver real value. You'll own key parts of our frontend stack, drive key architectural decisions, and turn complex security data into clear, actionable business insights.

In this role, you will design, build, and productionize advanced models that help organizations secure AI systems across data, models, and runtime usage. Youll work closely with Product, Engineering, and Security teams to turn complex, real-world security problems into scalable, high-impact AI solutions.

This is a high-impact, hands-on role with true ownership, where your work directly shapes the intelligence powering a category-defining AI Security platform.


What Youll Do:

Own End-to-End Model Development (Research → Production)

Take full ownership of the model lifecycle: problem definition, research, prototyping, training, evaluation, deployment, and ongoing optimization.

Design and build scalable ML / AI solutions to detect, analyze, and mitigate AI-related security risks.

Ensure models are robust, explainable, and production-ready in real-world enterprise environments.

Partner Deeply with Product & Engineering

Collaborate closely with Product Managers to translate customer pain points and security requirements into clear modeling objectives.

Work hands-on with engineering to integrate models into production systems and data pipelines.

Balance research depth with pragmatic execution and business impact.

Build AI Security Intelligence

Develop capabilities across areas such as AI behavior analysis, anomaly detection, model governance, data risk detection, and usage observability.

Work with structured and unstructured data, including logs, prompts, model outputs, and metadata.

Continuously iterate on models based on usage patterns, feedback, and emerging threat vectors.

Lead Research POCs & Innovation

Stay at the forefront of LLMs, applied ML, and AI security techniques and bring new ideas into the product.

Were looking for an experienced, rigorous Data Scientist to join our monetization data team in Tel Aviv. Youll lead dynamic price-floor optimization strategies for programmatic display advertising across our global inventory, build models and experiments that determine which on-page experiences we show each user, and develop strategies to maximize user lifetime value while strengthening engagement and retention. Youll also shape and manage the roadmap for our internal experimentation platform, enabling rigorous testing across the business. This is publisher-side data science, focused on our own audiences and those of our partners. The bar that matters most here is measurement, proving credibly that a change actually moved revenue before it ships.


Responsibilities:

Design and build models for supply-side monetization optimization, user lifetime-value, retention, and ARPU predictions, while measuring revenue impact.

Analyze large-scale event data and maintain metric foundations.

Partner with cross-functional teams to deploy models into production.

Communicate findings and business impact to stakeholders.

Provide technical leadership and mentorship.

Monitor industry trends in programmatic advertising and ad-tech.

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're looking for a Data Scientist to join our team. As a Data Scientist, youll be a key member of our AI team, designing, building, and deploying cutting-edge AI systems that leverage large language models (LLMs), unstructured data, and advanced ML techniques. Youll be instrumental in bringing AI from research to real-world production, delivering high-impact features that drive our security platform.

In this role, you'll have a unique opportunity to solve complex problems, work with large scale data and be part of the core team that shapes the future of AI in the company.


WHAT YOU WILL DO
Design and develop end-to-end AI solutions, from data ingestion and modeling to deployment and observability.
Build and fine-tune LLM-based applications to tackle complex cybersecurity challenges.
Own the full ML lifecycle - from ideation and experimentation to production readiness.
Collaborate with product, engineering, and security teams to turn AI research into user-facing features.
Work with large-scale unstructured data (e.g., logs, threat intel, alerts) to extract meaningful insights.
Evaluate and optimize AI system performance, scalability, and reliability.
Contribute to core architectural decisions related to AI and ML infrastructure.

We're looking for an AI Engineer to join our team. As an AI Engineer, youll be a key member of our AI team, designing, building, and deploying cutting-edge AI systems that leverage large language models (LLMs), unstructured data, and advanced ML techniques. Youll be instrumental in bringing AI from research to real-world production, delivering high-impact features that drive our security platform.

In this role, you'll have a unique opportunity to solve complex problems, work with large scale data and be part of the core team that shapes the future of AI in the company.


WHAT YOU WILL DO
Design and develop end-to-end AI solutions, from data ingestion and modeling to deployment and observability.
Build and fine-tune LLM-based applications to tackle complex cybersecurity challenges.
Own the full ML lifecycle - from ideation and experimentation to production readiness.
Collaborate with product, engineering, and security teams to turn AI research into user-facing features.
Work with large-scale unstructured data (e.g., logs, threat intel, alerts) to extract meaningful insights.
Evaluate and optimize AI system performance, scalability, and reliability.
Contribute to core architectural decisions related to AI and ML infrastructure.

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

We are looking for an experienced, independent team player who has great data & computer science skills, a passion for data, and excellent analytical and algorithmic skills.

This role is diverse, encompassing AI/ML, data analysis, and backend engineering.

You will play a crucial role within a mission-critical team responsible for managing the heart and brain of Sunbits primary products. This involves working on core systems, such as POS underwriting models, models for merchant operations, Fraud investigator AI agents and more.

You will participate in cutting-edge risk management systems that safeguard the loan product's financial operation, as well as operational systems, with a direct impact on its financial success.

Our AI/ML team is part of the R&D group, so you will work closely with engineers and product managers as well.

Key Responsibilities
Research and develop statistical behaviors, study domain-specific data.
Develop state-of-the-art machine learning models end to end, including development, deployment, and continuous improvement. Both in-weight learning and in-context learning, for risk / fraud / operations related projects. This includes integrating models into production services and ensuring compliance with regulatory processes (e.g. providing evidence for production model audits).
Operate backend infrastructure for training and deploying ML models, ensuring optimal performance and reliability.
Develop and maintain Python code for translating ML model outputs into financial decisions.
Analyze 15+ different data sources in order to train models and agents to catch fraudulent patterns.
Conduct analytical research on our models impact on the portfolio.
Strategize and implement changes to enhance portfolio performance.

We're looking for a Data Scientist to join our fast-growing Data Science team focused on AI-first features. This role sits at the core of how personalizes the sales experience, designing and deploying end-to-end ML models that move real business metrics. You'll collaborate closely with ML engineers, product managers, and data engineers, and join weekly sessions where the team shares research, tests new ideas, and learns together.
Hands-on experience using AI development tools such as Cursor or Claude Code is a must for this role.
we're building for builders. We build fast and AI-first, so we look for builders. By a builder, we mean someone who turns "maybe" into "done".
This role is based in Tel Aviv. We work in a hybrid model, with 3 days a week in the office.

We're hiring an exceptional Applied AI Scientist to join our team!
In this role, you'll design and deploy enterprise-grade AI agents that drive real business impact across core functions - Sales, Marketing, Customer Success, Customer Support, Risk, and Finance. You'll be building agentic systems that execute complex, multi-step business workflows end-to-end. The ideal candidate is a hands-on builder with deep AI expertise who thrives on turning complex business problems into reliable, production-grade AI systems.
In This Role, You Will Be Responsible For:
Enterprise AI Agent Development: Designing, building, and deploying AI agents that automate and augment high-value workflows across business functions - including lead qualification and outreach (Sales & Marketing), churn reduction(Customer Success), and Ticket deflection (Support).
Agentic Architecture & Orchestration: Owning the end-to-end agent stack - from tool use, memory management, and multi-step planning to human-in-the-loop escalation patterns, guardrails, and audit trails suited for an enterprise fintech environment.
Knowledge Systems: Building and maintaining RAG pipelines that ground agents in Tipalti's proprietary knowledge - product documentation, customer data, financial records, and internal playbooks - using vector databases, hybrid search, and re-ranking.
Evaluation & Continuous Improvement: Defining agent evaluation frameworks that measure task completion, accuracy, hallucination rates, latency, and business impact - then iterating on agent behavior based on real usage data and stakeholder feedback.
Cross-Functional Collaboration: Partnering with stakeholders across the company to define agentic use cases, translate business requirements into technical features, and deliver products that create measurable ROI - from data preprocessing through product deployment.