לוח משרות דרושים Data Scientist עבודה זמנית

Were looking for a talented Data Scientist to join our Visual Language Action (VLA) validation team. In this role, youll take part in designing and implementing innovative validation systems for Visual LLM-based modules that power next-generation autonomous driving technology. This is a unique opportunity to combine coding, research, and large-scale data analysis in a fast-growing, cutting-edge field.
What will your job look like:
Define problem statements, create and refine prompts in collaboration with internal customers.
Develop and implement QA and validation systems, including designing and building large-scale validation infrastructure and data pipelines for VLA-based autonomous driving solutions.
Run and evaluate LLM-based systems developed by other team members, including performance measurement and analysis.
Work with large-scale data and cloud-based infrastructures to support evaluation pipelines.
Collaborate closely with algorithm developers, researchers, and system engineers in a dynamic environment.

Were looking to engage a Data Scientist (Temporary for 6 months) to work closely with our dynamic and collaborative team in Tel Aviv. In this contracting role, you will shape strategic initiatives in the domain of AI and algorithm development, driving the creation of deep and innovative capabilities that push the boundaries of AI-driven solutions. By blending cutting-edge methods in Generative AI and Applied Machine Learning, youll play a pivotal part in defining our next generation of advanced, data-driven products and services.
A few of the things you will do:
Collaborative Partner: Work effectively with our team members, contributing expertise and fostering an environment where colleagues can thrive.
AI Development: Drive projects from focused, time-bound experimentation using scientific tools to delivering robust, production-ready solutions.
Full Solution Ownership: Take accountability for outcomes - gathering requirements, developing data-driven solutions, and ensuring smooth deployment and operation through best-in-class AI practices.

We are seeking an experienced and analytical Intelligence Methodologist to join our new Gen AI team .
You will collaborate with analysts, product managers, data scientists and developers to design and optimize investigative workflows, ensuring our solutions address real-world intelligence challenges effectively.
This is a unique opportunity to combine your expertise in intelligence methods with cutting-edge conversational Gen AI technology.
you will:
Fostering cross-functional communication and bridging collaboration between Product Management, Data Science, and Development teams to design knowledge structures, training logic, and investigative methodologies that drive LLM-based conversational AI.
Develop and curate high-quality domain-specific datasets for training and evaluation.
Communicate findings, progress to stakeholders, and provide recommendations for further enhancements.
Provide expert knowledge and insights to ensure the LLM agent understands and processes domain-specific language accurately.
Design, document, and refine methodologies for conducting intelligence investigations.
Train intelligence analysts on the use of methodologies and tools.
Create documentation and training materials to support effective adoption of workflows.
Collaborate with internal teams and external partners to understand operational requirements.

Thinking about where to invest your Hanukkah gelt? We offer the smartest option data -driven, validated credit models.
Were hiring a Validation data Scientist to join our team in a hybrid high-tech environment on the train line in Tel Aviv and help make ML credit models as reliable as the eight nights of Hanukkah.
This is a 9-month contract with the possibility of extension, perfect for someone who loves analytical challenges and working closely with cutting-edge ML models.
What youll do:
Validate ML credit models: assess performance, data quality, robustness, and conceptual soundness
Challenge model assumptions and suggest alternative approaches
Provide insights into model strengths and weaknesses and recommend practical solutions
Prepare thorough documentation for regulatory standards
Support ongoing monitoring and improvement of predictive models.