דרושים » תוכנה » Data Scientist

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.

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 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 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 are looking for a Senior Data Scientist to join a team of passionate engineers who build premium solutions at scale. Every day, we tackle ambiguous and stimulating challenges. We build and operate large-scale distributed machine learning systems that process 1B predictions per second during real-time auctions.

What will you do?
As a Senior Data Scientist, your mission will be to:

Improve existing algorithms & tools allowing to explore and analyze more and more data and provide accurate feedback on the Teads activities.
Develop new algorithms & new approaches to provide accurate predictions and drive new products by providing business insights.
Implement your algorithms and models end to end.
Collaborate with a variety of teams to develop services from design to production.
Make sure the software is in good hands by writing, running and automating tests (unit, functional, load...).
Keep up to date with the latest Machine Learning technologies to make sure we always use the best class algorithms according to the context.
Shape how billions of people discover and enjoy premium content by improving ad relevance, quality, and efficiency across Teads global publisher network.

Were looking for an experienced Product Analytics Manager to play a key role as part of the Product organization. The Product Analytics Manager will lead the domain and work closely with Product, R&D, Sales and Marketing, Customer Success, and BI to drive data-informed product and business decisions.
Key Responsibilities
Define product success metrics, usage benchmarks, and adoption KPIs across products and customer segments, ensuring alignment with clinical and business impact goals.
Own the end-to-end product analytics framework - including event design, tracking strategy, data validation, and ongoing data quality monitoring.
Perform hands-on deep-dive analyses, working directly with raw data and collaborating with R&D and BI to ensure robust data models, pipelines, and instrumentation.
Partner with Customer Success and BI to deliver customer-centric insights and support the evolution of the product data infrastructure.
Build dashboards and analytical models to support product and business decision-making, evaluate feature performance, and establish a continuous feedback loop between user behavior and product development - using data-driven insights to challenge assumptions and prioritize high-impact roadmap initiatives.
Quantify the clinical and economic impact of solutions by analyzing real-world encounter data to validate key performance markers such as reduction in cost of care, improved patient outcomes, and product utilization.
Translate complex analyses into clear, actionable recommendations for technical and non-technical stakeholders.
Provide professional leadership in analytics, including team management (if applicable) and contributing to the growth of the analytics function.

We are seeking an MLOps Engineer to help us grow our technical team's capabilities. The ideal candidate has relevant experience in data engineering, preferably within the AI field. Aviation industry experience would be a great addition.

You will be responsible for building and maintaining models and data pipelines that power our data science workflows. You'll play a crucial role in ensuring the accuracy, consistency, and efficiency of the data we use for model training and inference. This involves working with both structured and unstructured data from various sources, leveraging your expertise in data engineering and machine learning to create a robust and scalable system.

We are looking for an MLOps Team Leadto 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.

You'll work end-to-end: agent design, tool implementation, retrieval quality, integration into existing product UIs, cloud infrastructure, and evaluation/observability. The platform already ships to customers - you'll extend it, raise its quality bar, and help define where it goes next.

What you'll do

Design and evolve agents - build LLM agents with tool use, routing, and human-in-the-loop flows.
Implement tools and integrations - expose product capabilities to the agent, with multi-tenant context, via internal APIs and MCP servers.
Own retrieval quality - contribute to our RAG pipeline end-to-end: ingestion, embeddings, vector search, and reranking.
Define and evolve host integration contracts - collaborate with host application teams to integrate the assistant into product UIs built on different frontend stacks. You own the shared remote module and the integration API; host teams own their stacks.
Drive evaluation-led development - write evaluators (rule-based, LLM-as-judge, multi-turn), maintain CI eval gates, and use traces and feedback to debug production behavior.
Operate the platform - own deployments, observability, and the performance and cost of LLM-backed services.
Establish engineering practices** for AI-specific work: prompt versioning, eval coverage, testing, and code review.