Jobs at Fetcherr

Lead and manage a team of data scientists and engineers building scalable data infrastructure, pipelines, and machine learning products for price optimization. Responsibilities include architecture, batch and streaming workflows, data quality, testing, observability, model analysis, deployment, sprint planning, code reviews, mentoring, recruitment, and hands-on technical contributions.

Own strategic airline accounts across the US, serving as executive relationship leader and escalation point. Align AI deployments with client goals, drive account growth, proof-of-concept conversions, and renewals, and manage complex timelines, scope, crises, and deliverables. Coordinate Product, Data Science, Technology, and Support teams while engaging C-suite stakeholders. The role requires enterprise account leadership, strong negotiation, analytical rigor, and familiarity with airline commercial functions such as pricing, revenue management, network planning, PSS, and RMS systems.

Lead and develop a global IT support and systems team while defining IT strategy, managing infrastructure, SaaS applications, security systems, vendors, budgets, assets, and service delivery. The role oversees cloud platforms, MDM, identity management, networking, compliance, documentation, onboarding, and complex IT projects across remote and international environments.

Owns end-to-end Israeli payroll processing and oversees global payroll operations across international entities. Responsibilities include payroll audits, reconciliations, journal entries, accruals, benefits and pension data, employee payroll changes, tax filings, regulatory compliance, and coordination with HR, Finance, and external payroll providers. The role also resolves payroll discrepancies and improves processes in a fast-growing global technology company.

Leads a team of MLOps engineers building and operating machine learning infrastructure, data pipelines, and model-lifecycle systems. Owns initiatives from design through production, mentors engineers, establishes technical standards, and partners with R&D to deliver scalable research and production workflows. The role involves hands-on coding, distributed computing, pipeline orchestration, cloud platforms, Kubernetes, Docker, and migration from Dask to Ray.

Lead and mentor an MLOps engineering team while owning the design, development, and production operation of machine learning infrastructure and pipelines. Drive model-lifecycle initiatives, ensure reliable training and inference data, partner with R&D stakeholders, contribute hands-on code and technical direction, and help migrate distributed workloads from Dask to Ray.

Lead the transformation of infrastructure into a scalable, Kubernetes-native self-service platform. Build and grow the Platform Engineering team, establish infrastructure as code and GitOps practices, improve observability and reliability, modularize architecture, and enable secure, autonomous development workflows. The role requires hands-on cloud-native engineering across GCP services, Kubernetes, Terraform, Helm, and automation, along with mentoring, hiring, operational excellence, and blameless incident learning.

Leads a team of data scientists and engineers building scalable data platforms, pipelines, and machine learning products for real-time price optimization. Responsibilities include architecture, distributed batch and streaming workflows, data quality, testing, observability, model research, sprint planning, code reviews, hands-on development, recruitment, mentorship, and performance management.

Leads data scientists and engineers building scalable data infrastructure, pipelines, machine learning products, and price optimization systems. Responsibilities include architecture, batch and streaming workflows, data quality, testing, observability, sprint planning, code reviews, recruitment, mentoring, and hands-on development. The role requires strong Python, SQL, data modeling, orchestration, cloud platform, and people-management experience.

Lead the Platform Engineering team in transforming infrastructure into a scalable, Kubernetes-native, self-service platform. Own architectural modularization, infrastructure as code, GitOps delivery, observability, SLOs, reliability, and secure developer enablement. Mentor, hire, and grow engineers while establishing operational excellence, blameless learning practices, and sustainable delivery. The role remains hands-on and requires production-scale cloud-native expertise across Kubernetes, GCP services, Terraform, Helm, and platform tooling.

Lead and mentor an MLOps engineering team while building scalable machine learning infrastructure and production pipelines. Own end-to-end model lifecycle initiatives, including training, deployment, monitoring, and retraining. Partner with R&D teams to translate research needs into reliable systems, contribute hands-on code and architecture, and guide migration from Dask to Ray across distributed computing platforms.

Lead and mentor MLOps engineers building internal machine learning infrastructure, data pipelines, and model-lifecycle automation. Own initiatives from design through production, contribute hands-on code, establish engineering standards, and partner with R&D teams. Maintain scalable systems for training, deployment, monitoring, and retraining across distributed data platforms, including the migration from Dask to Ray.

Lead a team of data scientists and data engineers building scalable batch and streaming data pipelines, optimization systems, and machine learning infrastructure for real-time pricing. Own data architecture, quality, testing, observability, deployment, and architecture decisions while contributing hands-on to development. Manage sprint planning, code reviews, recruitment, mentoring, and team growth.

Lead a team of data scientists and engineers building large-scale batch and streaming data pipelines for real-time price optimization. Own data architecture, quality, testing, observability, orchestration, and deployment while contributing hands-on to architecture and development. Manage sprint planning, code reviews, hiring, mentorship, and performance feedback. The role requires Python, advanced SQL, cloud platforms, orchestration tools, distributed data processing, and experience managing data engineering teams.

Lead data scientists and data engineers developing scalable data platforms, distributed batch and streaming pipelines, and machine learning products for price optimization. Own data architecture, quality, testing, observability, and deployment practices while contributing hands-on to technical decisions. Manage sprint planning, code reviews, recruitment, mentorship, and performance feedback. Research pricing models and algorithms and ensure reliable, high-performance decision systems.

Lead a team of MLOps engineers building and operating scalable machine learning infrastructure, data pipelines, and model-lifecycle workflows. Own initiatives from design through production, contribute hands-on code, establish engineering standards, and partner with research teams. The role includes maintaining training and inference pipelines across distributed systems and supporting the migration from Dask to Ray.

Serve as the primary ferry revenue management and pricing authority, advising Product, Engineering, Data Science, Solutions, Commercial, and Customer teams. Define operational data requirements, business rules, pricing constraints, and modeling assumptions for ferry operations involving passengers, vehicles, accommodations, and freight. Support product discovery, customer implementations, workshops, commercial opportunities, and executive engagements while validating pricing outputs and sharing industry benchmarks, trends, and best practices.

Designs and maintains scalable, low-latency data pipelines for real-time and batch market data. Builds ETL/ELT processes, optimizes storage and streaming systems, supports productionizing AI and machine-learning models, and improves data quality, observability, reliability, and cost efficiency. Collaborates with data scientists, ML engineers, and product teams to evolve Fetcherr’s data platform.

Serve as the ferry revenue management and pricing authority for an AI-driven optimization product. Define operational data requirements, business rules, regulatory constraints, and modeling logic; validate pricing outputs; guide product and engineering teams; support customer implementations, workshops, commercial opportunities, and industry strategy. Expertise should cover passenger, vehicle, accommodation, and ro-ro freight inventory, demand forecasting, dynamic pricing, ancillary revenue, and ferry-specific KPIs.

Lead the production engineering organization responsible for the market model’s data pipelines, ML training and serving, infrastructure, monitoring, reliability, and costs. Set roadmaps and priorities across MLOps, ML engineering, and data engineering; manage functional leads; oversee delivery, KPIs, hiring, technical standards, and cross-functional collaboration. Partner with applied research to productionize models and communicate progress, risks, and technical decisions to executives, product, commercial teams, and engineers.