At PDF Solutions, we’re transforming the semiconductor and electronics manufacturing industry with our AI platform that improves yield and lowers manufacturing costs at some of the largest chip makers in the world. Not just machine learning, but AI. We’re seeking an experienced Data Scientist to join our team, who is responsible for developing model pipelines to enable and drive production of the world’s most advanced chips. We look for people who are self-motivated and passionate about the transformative potential of AI. You’ll be able to hone your skills while working side by side with industry experts who have decades of experience. The candidate must be an organized and highly motivated team player with strong initiative and communication skills, and possesses the drive to deliver quality results on time in a complex, intensive, and highly productive environment.
Help design, implement, and validate the ML Pipelines while collaborating with other data scientists.
Coordinate and collaborate with other Software Development group so that ML Pipeline fits well with the rest of PDF Solutions’ software applications.
Balance adding new features with the need for stability and performance.
Grow development capabilities to align with the pace of business needs.
QUALIFICATIONS AND SKILLS
Master's degree or higher in Computer Science, Computer Engineering, Electrical Engineering or similar discipline with industrial experience in software development
3+ years of experience with Python coding
3+ years of recent experience working as a Data Scientist in industry
Experience with developing production-grade code, preferably in Python
Experience with data science and machine learning, including Python libraries such as NumPy, SciPy, Pandas and Scikit-learn
Strong professional written and verbal communication skills
Ability to pass a Data Science skills-based test
Experience with relational or NoSQL databases such as Oracle/Cassandra/Redis or similar
Ability to create model-ready data from raw data, at scale
Ability to translate business problems into data science pipelines
Comfort with ML theory to recommend solutions beyond the standard libraries
Must be able to work independently and as part of a diverse interdisciplinary and international team
Communicates clearly to technical and non-technical audiences
Empathy with customer business challenges
Ability to map business problems to software and data science techniques
Understanding of fundamental data science and machine learning pipeline including data cleansing, feature engineering, imputation, model tuning, and model prediction
Basic understanding of the pros and cons of different machine learning algorithms, and basic understanding for different types of open source ML frameworks
Understanding of hypervisors/containers, especially Docker
Proficiency in both agile and waterfall development methodologies
Familiarity with Spark and TIBCO Spotfire
Knowledge/experience with backend, and ideally frontend as well, development/frameworks/libraries
Experience with Cassandra DB or other NoSQL DB a Plus