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We're seeking a highly motivated and client-focused Account Director to manage strategic relationships with key customers across multiple verticals. This individual will serve as the primary point of contact for assigned accounts, ensuring:
Exceptional client satisfaction.
Commercial performance.
Alignment between our product suite and each client's business objectives.
Key Responsibilities:
Serve as the day-to-day contact for strategic clients, managing relationships and ensuring high levels of satisfaction and engagement.
Understand client needs and business goals, and translate them into actionable plans that align with our capabilities.
Own execution of client initiatives and projects, ensuring action items are driven to completion, stakeholders remain aligned, and commitments are delivered on time.
Proactively identify opportunities to drive additional value for the client through product adoption, feature utilization, and cross-sell initiatives.
Support contract renewals and commercial discussions, working closely with internal stakeholders to deliver mutually beneficial outcomes.
Coordinate with internal teams (Product, Program, Tech, Support, and Data Science) to ensure successful solution delivery and issue resolution.
Monitor client performance metrics, analyze trends, and provide insights and recommendations for continuous improvement.
Represent the client's voice internally to influence product development and service enhancements.
Willingness to travel frequently to Genoa, Italy for client meetings, QBRs, and on-site engagements (approximately twice a month)**

Were looking for a Data Analyst with expertise in Tableau to join our growing data analytics team. This role is perfect for someone who thrives on turning data into actionable insights and enjoys working at the intersection of business and technology.
At Fetcherr, 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 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.