Data Scientist

SKF Group

Data Scientist building scalable AI solutions for SKF’s industrial machine-health products. Developing production machine-learning methods and partnering with engineers and domain experts.

Posted 8/24/2026full-timeYokneam • 🇮🇱 IsraelMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in data analysis, statistical modeling, and machine learning, with strong proficiency in Python and relevant libraries. Capable of collaborating effectively in cross-functional teams to deliver scalable AI solutions for industrial applications.

Highest-signal resume keywords

Python ProgrammingMachine Learning LifecycleData AnalysisStatistical ModelingCollaboration in Agile Environment

ATS Keywords

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Hard Skills

Data AnalysisStatistical ModelingMachine LearningSignal ProcessingData PreparationExperimentationValidationDeploymentProduction SupportTime-Series Analysis

Soft Skills

CollaborationCommunicationCuriosityProblem-SolvingAdaptability

Tools & Technologies

PandasNumPyScikit-learnGitHubAWSMLOpsCI/CDData PipelinesLarge DatasetsGenerative AI Systems

Industry Keywords

Data ScienceIndustrial EngineeringApplied MathematicsMachine HealthCondition Monitoring

Tech Stack

Tools & technologies

AWSCloudNumpyPandasPythonScikit-Learn

About the role

Key responsibilities & impact

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 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, vibration analysis experts, and domain experts
Communicate findings, recommendations, risks, trade-offs, and delivery status to technical and non-technical stakeholders
Investigate data quality issues and collaborate with data engineers to improve data availability, reliability, and usability
Help build scalable AI solutions for industrial machine health

Requirements

What you’ll need

3–5 years of experience
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 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 meaningful 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 with data engineers
Experience across the machine learning lifecycle, including data preparation, experimentation, evaluation, validation, deployment, and production support
Ability to work with ambiguous requirements, make technical trade-offs, and communicate assumptions and risks
Strong collaboration and communication skills in a cross-functional, fast-paced Agile environment
Ability to present recommendations and trade-offs to technical and non-technical stakeholders
Curiosity and ability to quickly learn new domains, technologies, and business contexts
Knowledge of time-series analysis or signal processing is nice to have
Experience with machinery sensor data or industrial condition monitoring is nice to have
Experience with cloud platforms, preferably AWS, is nice to have
Familiarity with MLOps, CI/CD, experiment tracking, model monitoring, and production ML systems is nice to have
Experience with large datasets and data pipelines is nice to have
Experience with LLMs, prompt engineering, RAG, AI agents, evaluation frameworks, or other generative AI systems is nice to have

Benefits

Comp & perks

No benefits, perks, or compensation extras are specified in the posting