We are looking for a Data Scientist to join our AI Development Center in Yokneam and help build scalable AI solutions for industrial machine health. This is a hands-on, mid-level role.
Success in this role requires balancing analytical rigor with practical delivery. We are looking for Data Scientists who enjoy turning ideas into working product capabilities, partnering closely with software and data engineers, and delivering measurable business value.
You will own defined data science work, shape the technical approach, plan and break down the work, and deliver it through completion in close collaboration with Product Owners, vibration analysis experts, data scientists, and software and data engineers.
What you'll do
Own defined data science problems or product capabilities from planning to delivery.
Break larger work into clear tasks, estimates, priorities, dependencies, and risks.
Track progress, communicate clearly, and adjust plans when needed.
Analyze data, model, and system performance to identify practical improvements.
Develop, test, and validate suitable analytical, machine learning, signal-processing, or optimization methods.
Write production-quality, maintainable Python code, participate in code reviews, and contribute to software delivered as part of a production system.
Define success measures and evaluate solution quality, usefulness, and business impact.
Collaborate with Product Owners, engineers, data scientists, and domain experts.
Communicate findings, recommendations, risks, trade-offs, and delivery status clearly to technical and non-technical stakeholders.
Investigate data quality issues and collaborate with data engineers to improve data availability, reliability, and usability.
Requirements
Bachelor's or master’s degree in data science, Statistics, Industrial Engineering, Computer Science, Applied Mathematics, or a related quantitative field.
Practical experience applying data analysis, statistical modeling, machine learning, or operational research to real-world problems.
Strong Python programming skills and hands-on experience with libraries such as Pandas, NumPy, and Scikit-learn.
Experience with GitHub or another version-control platform, including branching, pull requests, code reviews, merge conflict resolution, and collaborative development workflows.
Ability to plan and deliver a meaningful piece of work by breaking it into clear tasks, estimating effort, and following through.
Ability to investigate data quality issues, understand data flows and data pipelines, and collaborate effectively with data engineers to improve data availability, reliability, and usability.
Experience across the machine learning lifecycle, including data preparation, experimentation, evaluation, validation, and working with engineers to deploy and support production solutions.
Ability to work with ambiguous requirements, make reasoned technical trade-offs, and communicate assumptions and risks.
Strong collaboration and communication skills in a cross-functional, fast-paced Agile environment.
Present recommendations and trade-offs to technical and non-technical stakeholders, adapting communication to the audience.
Demonstrates curiosity and the ability to quickly learn new domains, technologies, and business contexts.
Nice to have
Knowledge of time-series analysis or signal processing.
Experience with machinery sensor data or industrial condition monitoring.
Experience with cloud platforms, preferably AWS.
Familiarity with MLOps, CI/CD, experiment tracking, model monitoring, and production ML systems.
Experience working with large datasets and data pipelines, on Data Science platforms.
Experience working with large language models (LLMs), prompt engineering, retrieval-augmented generation (RAG), AI agents, evaluation frameworks, or other generative AI systems.