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We are seeking an experienced Data Science Team Lead to lead our data science and data engineering efforts and oversee a team of skilled data scientists & engineers. This role combines hands-on technical leadership and team management, with responsibilities that include building and scaling data infrastructure to power real-time pricing, large-scale data pipelines, and machine learning products.
The Price Optimization (PO) team is where insights become decisions. We work with large-scale customer data and market predictions - like demand forecasts - to drive revenue management decisions that actually move the needle.
At our core, we build and maintain the decision-making engine. That means designing the data pipelines that bring customer data in, and running it through a price optimization decision model that simulates the market - weighing competition, pricing constraints, inventory availability, predictive models, and each client's unique business policies - to generate the best possible price recommendations. As the final step before recommendations reach the client, quality, reliability, and attention to detail aren't just nice to have. They're everything.
You will lead the team through architecture decisions, development, and deployment of mission-critical systems-while growing and mentoring a high-performing team.
Responsibilities:
Manage a team of data scientists and data engineers responsible for building robust, scalable, and high-performance data pipelines and infrastructure.
Analyzing existing models and algorithms, researching for new approaches in price optimization.
Design, build and maintain distributed data processing workflows (batch & streaming).
Drive best practices for data quality, validation, testing, and observability.
Own and evolve our data architecture in alignment with business and product goals.
Manage sprint planning, task breakdown, code reviews, and performance feedback for your team.
Contribute hands-on to key development tasks and architecture decisions.
Recruit, mentor, and grow the data science engineering team.

We're looking for a Data Scientist to help us grow our advanced analytics team's capabilities. The ideal candidate is a data scientist with a relevant experience who is self-driven, motivated, independent and sharp
Design and operationalize causal-uplift measurement frameworks to quantify the incremental impact of pricing models
Lead data discovery and translate complex multidimensional data into actionable insights related to the pricing algorithms and their outcomes
Conduct research and experimentation to elaborate novel approaches to uplift measuring and a/b testing
Collaborate with revenue managers, data analysts, data engineers in order to encode airline business logic into modeling and technical pipelines
The Insights & Decision Science (IDS) team is where data meets storytelling. We take the hard work of our AI pricing engine and translate it into something airlines can actually see, trust, and act on - think rigorous statistical analysis with a human touch. If a number moves, we explain why. If a client has a question, we have the answer (and the chart to back it up).

We are seeking a talented and self-driven experienced Data Scientist to help advance our machine learning capabilities.This is a key role for someone passionate about leveraging machine learning to solve complex, real-world problems and deliver measurable business impact.
Responsibilities:
Develop and implement state-of-the-art econometric and machine learning models for demand forecasting.
Conduct research and experimentation to evaluate novel approaches for improving accuracy, robustness, and scalability.
Collaborate with cross-functional teams (including product, data engineering, MLOPS and Platform) to deploy ML systems in production.
Clearly communicate complex technical findings to non-technical stakeholders, including product leaders and executives.

The Large Market Model group (LMM) is looking for a Director of Data Science to lead its production engineering, overseeing MLOps, ML engineering and Data engineering. The Director owns the end to end ML lifecycle of the market model, from the data pipelines that feed it, through model training and serving, and on to the monitoring that keeps it healthy once it is live in customer systems.
Crucially, the Director is responsible for setting the roadmap across these functions, alongside LMM management and senior executives, and for turning it into work each function can execute. We are looking for a manager who has led high paced, AI first groups, and who has carried deliveries for systems running live in front of customers.
RESPONSIBILITIES
Decide priorities across the functions with their leads, and review the technical trade-offs yourself.
Manage the leads who run these functions and hold each of them to what their team has committed to.
Stay close to production. Own delivery dates, reliability, and what happens when something breaks.
Own the infrastructure for model training, serving, and large scale data processing, including its cost.
Decide which numbers (KPIs) the group is measured on, then track them and report them.
Report the group's progress and risks to senior management, to product, and to the commercial teams.
Work with the applied research team to move their results into production.
Hire and keep senior engineers, and decide how the group is staffed.
Set the engineering standards the group works to: CI/CD, automated testing, observability, and model monitoring.

We are looking for an Engineering Team Lead to lead the MLOps efforts - drive the development of an internal machine learning platform for a group of ML teams. This is a hands-on leadership role: you will guide a small team of MLOps engineers that builds the automation and infrastructure powering our research (R&D) workflows and runs our pipelines to production. You will take ownership of end-to-end initiatives and drive the team toward critical infrastructure and model-lifecycle milestones, while staying close enough to the code to set technical direction and raise the bar by example.
You will be responsible for building and maintaining the models and data pipelines behind our data science workflows, ensuring the accuracy, consistency, and efficiency of the data used for training and inference, working across structured and unstructured data from many sources on a large-scale, distributed platform.
Responsibilities:
Lead, mentor, and grow a team of MLOps engineers, owning delivery and technical quality.
Take end-to-end ownership of infrastructure and pipeline initiatives across the LMM group, from design through production.
Stay hands-on: contribute to design and code, review work, and set engineering standards.
Drive the team through critical milestones in ML model-lifecycle and infrastructure ownership.
Partner with R&D and other stakeholders to translate research needs into robust, scalable systems.
Help evolve the platform, including our ongoing migration from Dask to Ray.

We are seeking an experienced Algorithm Team Lead - Optimization to lead our data science and data engineering efforts and oversee a team of skilled data engineers. This role combines hands-on technical leadership and team management, with responsibilities that include building and scaling data infrastructure to power real-time pricing, large-scale data pipelines, and machine learning products.
The Price Optimization (PO) team is where insights become decisions. We work with large-scale customer data and market predictions - like demand forecasts - to drive revenue management decisions that actually move the needle.
At our core, we build and maintain the decision-making engine. That means designing the data pipelines that bring customer data in, and running it through an optimization engine that simulates the market - weighing competition, pricing constraints, inventory availability, predictive models, and each client's unique business policies - to generate the best possible price recommendations. As the final step before recommendations reach the client, quality, reliability, and attention to detail aren't just nice to have. They're everything.
You will lead the team through architecture decisions, development, and deployment of mission-critical systems-while growing and mentoring a high-performing team.
Responsibilities:
Manage a team of data scientists and data engineers responsible for building robust, scalable, and high-performance data pipelines and infrastructure.
Design, build, and maintain distributed data processing workflows (batch & streaming).
Drive best practices for data quality, validation, testing, and observability.
Own and evolve our data architecture in alignment with business and product goals.
Manage sprint planning, task breakdown, code reviews, and performance feedback for your team.
Contribute hands-on to key development tasks and architecture decisions.
Recruit, mentor, and grow the data science engineering team.

Required Senior DevOps Engineer
About The Position
We are scaling rapidly, and we are transforming our internal infrastructure into a high-velocity, self-service product. This is a senior opportunity to architect the future of our platform, setting the technical standard for how we deliver services while ensuring reliability and developer independence across diverse global markets.
Responsabilities:
Self-service enablement: Build secure, automated pathways that accelerate development while keeping organizational standards intact.
Architectural modularization: Design Kubernetes-native abstractions - controllers and operators - and decouple infrastructure into independent service lifecycles that support rapid iteration.
Everything as code: Treat infrastructure, policy, and configuration with the same SDLC as application software - versioned, reviewed, tested, and CI-gated.
Proactive reliability: Own production through robust observability, clear SLOs, and high operational hygiene; define alerting that fires correctly and lead blameless postmortems that fix the system.

We are looking for Customer Support Engineer in our site in Netanya with a strong data background, who can provide ongoing support to our client while monitoring our production pipeline ensuring we deliver within the agreed SLA . You will be an essential part of the frontline support team responsible for providing prompt and efficient support and service to end-users experiencing technical issues with software. The primary responsibility is to ensure the timely resolution of data-related problems and to facilitate effective communication between end-users and higher-level support teams. The ideal candidate has relevant experience in data engineering. This role involves coordinating with various teams, managing schedules, and ensuring that production processes run smoothly and efficiently.
Work schedule:
The employee will be required to work 5 days a week, spread across the entire week, including Saturday and Sunday, with off-days during weekdays if shifts fall on weekends. The job is shift-based, with shifts occurring both during the day/evening/night and on weekends.
What You'll Do:
You will be responsible for maintaining and monitoring data pipelines that power our data science workflows while developing and maintaining production schedules.
Identify potential production issues and work proactively to resolve them.
Communicate any production delays or issues to relevant stakeholders and customers.
Maintain accurate records of production activities, including schedules, bugs and project status.
Coordinate with various departments (e.g., Data, DE, PO) to ensure timely completion of tasks.
Serve as the frontline support and provide prompt and efficient support to end-users experiencing technical issues with the platform.
Troubleshoot problems and offer effective solutions to maintain optimal user experiences.
Collaborate with other teams, including R&D and product management, to resolve customer issues.

We are looking for an SVP of Product to own and lead our product function. This is a hands-on leadership role for a strong product operator who can drive strategy and execution in equal measure - managing a growing team of Product Managers and different domains SMEs, working closely with customers and internal stakeholders, and delivering complex AI-driven products that create measurable value for the world's leading airlines. This role reports directly to the CEO.
What You'll Do:
Lead and execute our product strategy and roadmap, ensuring alignment with company goals, customer needs, and commercial priorities.
Manage, mentor, and grow a team of Product Managers, setting clear expectations, building strong product practices, and driving accountability.
Own the full product lifecycle - from discovery and definition through delivery and iteration - working hands-on with Engineering, Data, and Design.
Be the product voice in customer relationships: lead roadmap reviews, gather requirements, and present product direction to enterprise stakeholders.
Partner with Sales and Customer Success to support onboarding, renewals, and expansion by ensuring the product delivers on its promise.
Translate complex AI and algorithmic capabilities into clear, user-friendly experiences and compelling value propositions.
Collaborate with company leadership on business priorities, surfacing product insights that inform strategy and investment decisions.
Drive execution discipline - managing timelines, trade-offs, and delivery in a fast-moving, resource-conscious environment.

Were looking for a System Architect to own the technical foundation of the Labs product end to end, from the whiteboard to production on Azure. Working closely with our Chief AI Officer, youll translate complex research assumptions and decisioning methodologies into clean, self-service products. You will port the right pieces of our engine into a lean new stack, make it integrate cleanly across the business environments our customers already run, and lead the coding yourself. This is a hands-on architect role, not an oversight role: you set the design and youre in the codebase every day.
A core part of the mandate is a fully agentic SDLC, building the product with AI coding agents end to end, not just building an agentic product. We expect a small team to ship like a large one by making agent-driven development the default way we design, write, and review code. Youll define how we do that.
What youll do:
Translate methodology into product. Partner with the Chief AI Officer to turn complex assumptions, math, and decisioning methodologies into clean, self-service products a customer can run without a data-science team.
Own the architecture. Design a scalable, cloud-native system on Azure, covering services, data flow, model-serving and the end-to-end decision pipeline, and keep it coherent as it grows.
Lead the coding. Set the standards, make the hard build-vs-buy calls, and write the core code yourself. Youre the technical center of gravity for the team. You will guide and work shoulder-to-shoulder with the other engineers through the architecture and the code, without managing them directly. Leadership here is by design and example, not by org chart.
Stand up a fully agentic SDLC. Establish how we design, generate, review, and ship code with AI agents end to end: the tooling, guardrails and CI gates that let a small team punch far above its size.
Stand up the environment. Establish a fresh, greenfield Azure environment for Labs on a Kubernetes-native, Everything-as-Code foundation: IaC (Terraform, Helm), GitOps (ArgoCD or similar) and CI/CD from day one. In the early days that means rolling up your sleeves on the platform groundwork (subscriptions, networking, environments), working hands-on alongside Microsofts teams to get us live.