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As a Senior Data Scientist , you will be an integral part of our Machine Learning and AI team. The role is hands-on and impact-driven, focusing on building, improving, and delivering machine learning models that directly support business goals.

Responsibilities
Design and deploy production ML systems that drive automated business decisions (pricing, bidding, resource allocation)
Build end-to-end ML pipelines - from data ingestion and model training to serving, monitoring, and incident response
Translate ambiguous business problems into rigorous mathematical frameworks and own them from conception to production impact
Conduct rigorous experimentation (A/B testing, causal inference, uplift modeling) to measure and improve model performance
Maintain and improve real-time models that adapt to incoming data and feedback signals
Mentor junior team members on ML best practices and production standards

we are seeking a Senior Data Analyst position to join the BI Team, with a passion for technology, to solve unique problems within our measurements. Our team solves many challenges in turning raw data into accurate signals and extracting insights to improve the 365Scores products. Appropriate candidates have an understanding of multiple components across the big data stack: large-scale data analysis, data quality, statistics, and visualization. If you are the kind of researcher who's focused on results and a quick learner, this role is for you. This position is located in our Tel Aviv office.

Responsibilities
Function as the data authority in the Monetization domain - performing ad-hoc research and analysis on the company's rich data to support unique and strategic business questions.
Define, analyze, and monitor KPIs and OKR metrics to continually improve company products.
Analyze large, complex data sets to address strategic and operational business questions.
Actively take part in building and maintaining our AI Driven-BI Agentic Architecture.
Build dashboards and visualizations to answer key questions for the company's in-house various teams, specifically for the Monetization Team.
Perform qualitative, quantitative, and statistical analysis.
True passion for understanding data and finding Insights.
Provide actionable recommendations and conclusions from data to shape features and future product road maps.

Required Data Engineer
About The Position
Design, build, and maintain scalable data pipelines and infrastructure entirely within GCP
Own data solutions end-to-end, from architecture and design through to production delivery
Build and maintain custom connectors to advertising platforms including Google Ads, Meta (Facebook) Marketing API, and other ad platforms
Design and implement data warehouse structures with a deep focus on performance, scalability and usability
Continuously investigate and evaluate new GCP native tools and services, assessing their fit, building proof of concepts, and driving adoption where it makes sense
Set up and configure orchestration and transformation frameworks (such as Airflow and dbt) within the GCP environment
Collaborate closely with backend engineers to ensure data infrastructure aligns with product and system needs
Contribute to data science initiatives and bring genuine curiosity to analytical use cases
Monitor, troubleshoot, and continuously improve the reliability and performance of existing data systems
Define technical standards and best practices for the data platform
Build and maintain data dashboards and reports to surface insights for stakeholders, primarily using Looker Studio.

As a Data Scientist, you will be responsible for the entire algorithmic lifecycle of our company's solution. From analyzing large scale customer data to shipping new models to production, your responsibilities will be end-to-end.
Mentoring and coaching team members is an essential aspect of your position.
The role is a hybrid work model from our offices in Tel Aviv.
Key Responsibilities
Collaborate with the product team and occasionally engage with customers to gather requirements for developing and delivering new predictive solutions.
Analyze large volumes of diverse data sets and develop algorithmic solutions and models to solve complex problems.
Turn algorithm prototypes into shippable products that have a significant and immediate impact on our company's success.
Collaborate with software engineers to develop scalable solutions.
Take end-to-end ownership of projects and ensure successful delivery.
Continuously improve our tech stack and development practices.
Mentor and coach team members as a domain expert.

As a AI-Data Engineer, youll take a leading role in building the foundation of our companys Agentic Exposure Management platform: the source of truth that tells a security team what they have, whats actually risky and why, how to fix it, and who is responsible.
Enterprises run their security across a sprawl of cloud and on-prem tools. Making sense of that sprawl is the heart of what we do. Were building a complex data fabric that reconciles data from a hundred different sources, applies sophisticated transformation and enrichment, and turns partial, uncertain signal into one trustworthy model of an organizations real exposure.
Key Responsibilities:
Model the security graph. Design an ontology that spans both the modern cloud and the classical on-prem world, abstract enough to absorb 100+ different integrations under one model, yet rich enough to carry everything needed for decision making. Decide whats an entity, whats an edge, and whats a denormalized attribute, across millions of assets and finding rows.
Own asset identity engineering and reconciliation. Upstream identifiers are uncertain and volatile. Engineer deterministic identity resolution for assets and findings arriving from different vendors.
Build LLM-based and agentic pipelines for data classification, entity extraction, and enrichment, optimizing for cost, latency, and precision in production.
Build the integration factory. Stand up a scalable integration platform that drives down time-to-integration.
Build and maintain complex ETL pipelines handling millions of records: incremental sync, complex correlation, and data quality observability and monitoring.

We are seeking a visionary and hands-on Staff AI Scientist to help shape the next generation of foundational language models and agentic systems that empower small and medium-sized businesses to thrive. In this role you will define, design, and build groundbreaking AI solutions that understand, reason, and act across complex financial and business contexts.
You will collaborate closely with cross-functional experts across 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.
As a Staff Scientist you set technical direction beyond your own projects. You are a builder-scientist - a creative thinker who develops novel model architectures and training paradigms and also drives their implementation and validation in real-world applications - and you raise the scientific bar for the teams around you. You will help shape the AI foundation that powers transformative products serving millions of customers.
Responsibilities:
Set technical direction. Define the technical strategy and roadmap for next-generation agentic and foundation-model systems, and drive innovation and scientific excellence across the organization.
Build foundational models and agents. Design and develop foundational language models and agentic architectures that power intelligent decision-making for small and medium businesses.
Advance the science. Explore novel approaches in large-scale pretraining, fine-tuning (SFT and RLFT/GRPO), retrieval-augmented generation, and reasoning to improve factuality, adaptability, and alignment.
Solve real financial work. Build intelligent agents that understand financial data, customer context, and business workflows - from bookkeeping and autonomous accounting to planning, communication, and insight generation.
Leverage our data moat. Turn our unique ecosystem signals - counterparty behavior, two-sided transaction visibility, and business twins - into defensible modeling advantages.
Ship end-to-end. Take work from experimentation to production, owning evaluation frameworks and metrics that prove real-world impact.
Elevate others. Mentor scientists and engineers, review technical designs, and grow the craft of the wider team - a core expectation at the Staff level.
Collaborate across functions. Partner with AI research, engineering, product, and design to translate breakthrough science into scalable products used by millions.
Share knowledge. Publish and present research findings, internal innovations, or contributions to open-source frameworks where appropriate.

We are seeking a highly skilled and analytical Senior Data Engineer to join our Data team. In this role, you will design and implement robust data pipelines, while uniquely bridging the gap between engineering and analytics by actively analyzing data to extract actionable insights. You will play a crucial part in architecting our data foundations to support everything from business intelligence to advanced machine learning and agentic AI pipelines.
As a Senior Data Engineer, you will collaborate closely with engineering teams, product managers, and stakeholders across the organization. You will not only build the infrastructure utilizing modern data stack tools but also act as a data analyst when needed, ensuring our systems are fully equipped to operate within and support a cutting-edge agentic AI environment.
Responsibilities
Design, build, and maintain highly scalable ELT/ETL data pipelines.
Architect and manage modern cloud data warehousing solutions.
Develop, maintain, and monitor Python services responsible for robust data collection and ingestion.
Perform hands-on data analysis to interpret complex datasets, identify trends, and deliver business insights, acting in a dual capacity as a Data Analyst.
Develop and optimize data infrastructure specifically designed to support autonomous agentic workflows and LLM integrations.
Collaborate with engineers and analysts to troubleshoot data issues, enforce quality SLAs, and define data requirements.
Document data architecture, flow, and analytics standards for internal team alignment.
Build and maintain dashboards and reports to communicate analytical findings and data health to the organization.
Maintain Kafka consumer applications that process high-volume event streams in real-time, ensuring reliable ingestion into cloud databases.

We built a platform that builds businesses. From launching and managing to growing a business, we are the all-in-one solution that empowers any business owner. Our platform services over 40 million small businesses. Our "Guidance Engine" assesses each new business introduced to our platform and devises a tailored plan that allows you to manage all your business needs from a single dashboard. Through our business-building platform, were turning the process of starting, managing, and growing a business into a better experience; were simplifying the business journey. At Tailor Brands, we believe in more than just handing you another tool; we are dedicated to teaching you the art of building a business.
About the Role:
As a data Engineer, you'll design and maintain the data pipelines that power decision-making across product, marketing, and finance. Working with Apache Airflow, Python, and SQL, you'll build trusted, well-documented datasets that enable self-service analytics for the entire organization. You'll champion data quality through validation and monitoring, optimize infrastructure for scale, and be part of a forward-thinking team that actively leverages AI tools like Claude to push the boundaries of modern data engineering. If you thrive on turning messy data into reliable, actionable foundations - and want to do it for a platform serving over 40 million small businesses - this role is for you. What were looking for:
* Design, implement and maintain pipelines that deliver data with measurable quality and SLAs for our product, marketing, finance, and other departments
* Partner with business domain experts, data analysts, and engineering teams to build foundational datasets that are trusted, well-understood, aligned with business strategy and enable a self-service analysis
* Focus on data quality, detect data /analytics quality issues, implement bug fixes and data validations for prevention
* Identify, design, and implement internal process improvements such as automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc
* Be part of a team that actively integrates AI tools like Claude into day-to-day workflows, pushing the boundaries of how data engineering gets done

As a Data Engineer, youll collaborate with top-notch engineers and data scientists to elevate our platform to the next level and deliver exceptional user experiences. Your primary focus will be on the data engineering aspects-ensuring the seamless flow of high-quality, relevant data to train and optimize content models, including GenAI foundation models, supervised fine-tuning, and more.

Youll work closely with teams across the company to ensure the availability of high-quality data from ML platforms, powering decisions across all departments. With access to petabytes of data through MySQL, Snowflake, Cassandra, S3, and other platforms, your challenge will be to ensure that this data is applied even more effectively to support business decisions, train and monitor ML models and improve our products.


Key Job Responsibilities and Duties:

Rapidly developing next-generation scalable, flexible, and high-performance data pipelines.

Dealing with massive textual sources to train GenAI foundation models.

Solving issues with data and data pipelines, prioritizing based on customer impact.

End-to-end ownership of data quality in our core datasets and data pipelines.

Experimenting with new tools and technologies to meet business requirements regarding performance, scaling, and data quality.

Providing tools that improve Data Quality company-wide, specifically for ML scientists.

Providing self-organizing tools that help the analytics community discover data, assess quality, explore usage, and find peers with relevant expertise.

Acting as an intermediary for problems, with both technical and non-technical audiences.

Promote and drive impactful and innovative engineering solutions

Technical, behavioral and interpersonal competence advancement via on-the-job opportunities, experimental projects, hackathons, conferences, and active community participation

Collaborate with multidisciplinary teams: Collaborate with product managers, data scientists, and analysts to understand business requirements and translate them into machine learning solutions. Provide technical guidance and mentorship to junior team members.

As a Senior Data Engineer, youll collaborate with top-notch engineers and data scientists to elevate our platform to the next level and deliver exceptional user experiences. Your primary focus will be on the data engineering aspects-ensuring the seamless flow of high-quality, relevant data to train and optimize content models, including GenAI foundation models, supervised fine-tuning, and more.

Youll work closely with teams across the company to ensure the availability of high-quality data from ML platforms, powering decisions across all departments. With access to petabytes of data through MySQL, Snowflake, Cassandra, S3, and other platforms, your challenge will be to ensure that this data is applied even more effectively to support business decisions, train and monitor ML models and improve our products.



Key Job Responsibilities and Duties:

Rapidly developing next-generation scalable, flexible, and high-performance data pipelines.

Dealing with massive textual sources to train GenAI foundation models.

Solving issues with data and data pipelines, prioritizing based on customer impact.

End-to-end ownership of data quality in our core datasets and data pipelines.

Experimenting with new tools and technologies to meet business requirements regarding performance, scaling, and data quality.

Providing tools that improve Data Quality company-wide, specifically for ML scientists.

Providing self-organizing tools that help the analytics community discover data, assess quality, explore usage, and find peers with relevant expertise.

Acting as an intermediary for problems, with both technical and non-technical audiences.

Promote and drive impactful and innovative engineering solutions

Technical, behavioral and interpersonal competence advancement via on-the-job opportunities, experimental projects, hackathons, conferences, and active community participation

Collaborate with multidisciplinary teams: Collaborate with product managers, data scientists, and analysts to understand business requirements and translate them into machine learning solutions. Provide technical guidance and mentorship to junior team members.

Were looking for a Technical Integration Manager to join our team and help connect the dots between our business goals and R&D efforts. This hybrid role is ideal for someone who enjoys working between business and technology, combining project coordination with technical understanding.
Youll be the main connection between our business units and R&D - understanding business needs, translating them into clear requirements, aligning priorities, and helping teams deliver successfully. Youll also support product and project activities to move initiatives forward.

Responsibilities

Serve as the main point of contact between business stakeholders and R&D
Translate business needs into clear requirements and technical briefs
Work closely with R&D to ensure alignment on scope, timelines, and delivery expectations
Own development initiatives end-to-end spec, tasks, QA, rollout, and monitoring whether you build them yourself using AI tools, automation and no-code, or hand them to R&D developers.
Write technical specs and acceptance criteria, and manage the work in task management tools.
Guide and train the business departments on new features documentation, walkthroughs, and ongoing support
Coordinate cross-functional meetings, updates, and ongoing communication
Assist in planning and prioritizing roadmaps and project timelines
Track and report on progress, risks, and blockers across multiple initiatives
Solve complex tech problems and support the business departments
Build new dashboards for internal and external users using tools like Google Data Studio, Redash, Tableau and others.

We are looking for a Senior Data Scientist to lead the development of machine learning solutions powering real-time decision-making and recommendation systems in industrial production environments.

This is a hands-on role with end-to-end ownership. You will be responsible for the full modeling lifecycle, from problem framing and data exploration to modeling, evaluation, deployment, and production impact. You will work with complex, noisy sensor and control data, building models that must be robust, interpretable, and reliable in real-world conditions.

You will collaborate closely with deep learning researchers, research engineers, and domain experts, and play a key role in shaping how data science is applied across the system.
Key Responsibilities


Design, develop, and evaluate machine learning models for real-time industrial applications
Own the end-to-end modeling pipeline, including problem formulation, data preparation, feature engineering, model development, evaluation, deployment, and post-production monitoring
Translate ambiguous production challenges into well-defined data science problems
Lead feature engineering and data exploration on complex sensor and control datasets
Define KPIs, evaluation frameworks, and experimentation strategies
Ensure model robustness over time, including monitoring, drift detection, and continuous improvement
Work closely with data and research engineers to bring models into production
Drive best practices in modeling, validation, and reproducibility
Communicate insights, trade-offs, and recommendations clearly to stakeholders

We are hiring an Engineering Manager to own and grow the Data Science group - the R&D team behind our company's security and safety AI models and the data platform that powers them. You will lead a multidisciplinary team of ML engineers, data scientists, and software engineers who ship real-time inference at scale, automated red-teaming of GenAI systems, and the Databricks/Spark data platform underneath. This is a hands-on people-leadership role: you set technical direction, are accountable for delivery and quality, and you build and grow the team.
What your team owns
The group is responsible for a large Python + Rust + PySpark monorepo (dozens of production services and shared libraries) spanning three connected domains:
1. Content-moderation inference at scale
Real-time, multi-tenant detection across text, image, video, and audio - hate speech, CSAM, nudity, child grooming, extremism, PII, prompt injection, age estimation, and more - served through an in-house ActiveServe framework over NVIDIA Triton and a Rust detection monolith, on latency-sensitive, SLA-bound, customer-facing traffic with per-customer custom models.
2. GenAI safety & red-teaming
Automated red-teaming that attacks customers' LLM applications with a research-driven attack taxonomy and measures attack-success rate, alongside the defensive side - LLM-as-judge escalation to cut false positives and the tooling that authors and refines moderation policies - built on a multi-provider LLM foundation (Bedrock, Anthropic, OpenAI, Gemini, xAI Grok) and forming our company's leading edge into agentic-AI safety.
3. Data platform & MLOps
An end-to-end lakehouse and MLOps stack on Databricks - bronze/silver/gold ingestion, PySpark pipelines, and model training, versioning, and promotion through MLflow / Unity Catalog into Triton serving - with the performance-critical hot paths engineered in Rust (PyO3/maturin) for sub-millisecond, high-QPS matching and detection.
What you'll do
Lead and grow the team - mentor ML engineers, data scientists, and software engineers, and own hiring, onboarding, 1:1s, career development, and performance.
Set direction and deliver - set technical direction and standards, turn company and product goals into a prioritized roadmap, and own the quality, reliability, and delivery of the systems above across parallel workstreams.
Champion excellence and partnership - stay hands-on to review designs and unblock the team, drive engineering excellence (testing, observability, CI/CD, on-call, cost/latency), keep the team at the state of the art in ML, LLMs, and GenAI safety, and partner with Product, Platform, and GenAI-safety stakeholders.
Leadership competencies
People-first: builds trust, grows engineers, and creates a healthy, inclusive, high-ownership culture.
Outcome-oriented: drives clarity, sets priorities, and delivers under ambiguity without micromanaging.
Technical credibility: earns the team's respect through sound judgment on architecture and trade-offs.
Systems thinker: balances short-term delivery against long-term platform health, cost, and tech debt.

We are seeking an experienced and passionate BI Developer with a strong background in Data Analysis to join our growing Data & Analytics team. In this role, you will own the entire BI development lifecycle-delivering scalable, reliable BI solutions and actionable insights.

You will play a key role in developing a robust data infrastructure, building intuitive dashboards, and ensuring data integrity across all reporting layers. The role is ideal for candidates who are adept at balancing hands-on development work and data analysis. You will thrive in this position if you enjoy solving complex problems, simplifying intricate data pipelines, and translating business needs into efficient data models and visualizations.

Core Responsibilities
‍Dashboard Development - Design, implement, and iterate KPI‑oriented Tableau dashboards that expose product, growth, and engagement signals.‍‍
‍ETL / ELT Engineering - Partner with data engineers to architect and orchestrate ELT pipelines. Youll own the analytics layer and semantic definitions.
Hands‑on experience with Amazon Redshift and dbt is a significant advantage.
‍Data Quality & Governance - Deploy validation tests, reconciliation queries, and lineage tracking to guarantee accuracy, consistency, and traceability across curated datasets and published metrics.‍
Data Analysis - Perform deep-dive analysis on dashboards to generate meaningful, actionable insights.

Were looking for a Director of Data to lead our evolving Data organization. This is a unique opportunity for an experienced technical leader who thrives at the intersection of leadership and execution. In this role, youll shape the future of our data platform and infrastructure while remaining closely connected to the technology, architecture, and innovation that drive our business forward.
Why Join Us
Lead a strategically important Data organization during a transformative phase of growth and evolution.
Shape the technical vision, operating model, and future direction of Data Engineering and Data Infrastructure.
Work hands-on with modern data technologies, AI-driven workflows, and emerging platforms while operating at a senior leadership level.
Influence critical company-wide initiatives and partner closely with leaders across R&D, Product, Finance, Support, and GTM.
Join a culture that values ownership, technical excellence, continuous learning, and meaningful impact.
Key Responsibilities
Lead our Data organization in its next phase, driving a lean, high-performing, and highly technical operating model.
Own the technical vision, architecture, and execution across Data Engineering and Data Infrastructure.
Directly manage and mentor a multidisciplinary team, fostering growth, accountability, and engineering excellence.
Provide strong hands-on technical leadership, guiding architectural decisions, reviewing designs, and driving execution on critical initiatives.
Partner with stakeholders across the organization to deliver scalable data products and platforms that support business and product needs.
Drive modernization of our data ecosystem, balancing proven data technologies with emerging approaches in AI, automation, and modern data platforms.
Lead strategic initiatives involving critical infrastructure, platforms, and investments, ensuring scalability, reliability, and operational efficiency.
Champion best practices in data engineering, platform reliability, observability, and operational excellence.