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Tier-1 brand plus broad ML/big-data requirements create moderate candidate competition.
Core ML and big-data skills transfer across industries, but semiconductor/MES experience raises specificity.
Extensive mandatory ML, big-data, and tooling requirements imply strict technical filtering.
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Apply machine learning and statistical modeling techniques on large-scale industrial manufacturing data (terabytes to petabytes).
Extract, cleanse, and analyze data from multiple databases using SQL and other query languages.
Develop software and implement advanced analytics in a production environment with tools including Python/R, Spark, and TensorFlow.
Work Experience Required: Not explicitly mentioned in the JD.
Strong skills in data extraction (SQL), software development, and statistical/machine learning methods.
Fluency in Python and/or R; experience or willingness to learn pySpark/SparkR, Hadoop (Hive, Spark, HBase), TensorFlow, and SQL databases such as Teradata.
Not explicitly mentioned: formal degree requirements, notice period, or mandatory semiconductor industry experience (exposure is a plus).
Experience or strong interest in advanced analytics within highly automated industrial manufacturing environments.
Comfort working with large, complex datasets including time-series and image data and handling semi-supervised or evolving data distributions.
Ability to translate complex data problems into analytical and software solutions with familiarity or interest in diverse tools spanning analytics, data engineering, and visualization platforms.