OverviewKeysightis on the forefront of technology innovation, delivering breakthroughs and trusted insights in electronic design, simulation, prototyping, test, manufacturing, and optimization. Our ~15,000 employees create world-class solutions in communications, 5G, automotive, energy, quantum, aerospace, defense, and semiconductor markets for customers in over 100 countries. Learn moreabout what we do.
Our powerful,award-winningculture embraces a bold vision of where technology can take us and a passion for tackling challenging problems with industry-first solutions.Diversity, equity & inclusionare integral parts of our culture and drivers of innovation at Keysight. We believe that when people feel a sense of belonging, they can be more creative, innovative, and thrive at all points in their careers.
ResponsibilitiesEnd-to-end delivery of data analytics projects including project framework, analysis technique and implementation of data science models.
Analyse and interpret large volume and complex datasets to extract insights, trends, and patterns, helping organizations make data-driven decisions.
Integrate external factor and global context into analytical models to enhance predictive accuracy and provide a broader context for decision making.
Apply various data science technologies: statistical analysis, machine learning and pattern recognition with subject-specific modelling to address opportunities in business performance and achieve supply chain efficiencies.
Design experiments and statistical tests to validate hypotheses.
Present findings to non-technical stakeholders in a clear and understandable manner.
QualificationsA bachelor's degree in a quantitative field such as Data Science, Statistics, Mathematics, Physics, or related fields, with a minimum of5 yearsof working experience.
Strong background in statistics and probability theory is essential for analyzing and interpreting complex data.
Proficiency in programming languages commonly used in data science, such as Python or R. Familiarity with tools like SQL for database querying is also valuable.
Understanding and practical experience with machine learning techniques and algorithms. This includes supervised and unsupervised learning, regression, classification, clustering, and more.
Ability to create meaningful visualizations to communicate complex findings effectively. Familiarity with tools like Matplotlib, Seaborn, or Tableau can be beneficial.
Strong communication skills to explain complex technical findings to non-technical stakeholders and decision-makers.
Having supply chain knowledge will be an added advantage.
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