Description

We are seeking a motivated and hands-on Data Scientist to join our team and help develop data-driven and AI-enabled solutions in the semiconductor domain. This role focuses on building practical machine learning applications, from proof-of-concept through production, while collaborating closely with cross-functional engineering teams.
You will work on transforming complex datasets into actionable insights, developing scalable models, and integrating AI/ML capabilities into real-world workflows and tools.

The Role

Our team develops analytical and machine learning solutions that support engineering, testing, and performance optimization efforts across multiple domains. We emphasize rapid prototyping, iterative development, and close collaboration with stakeholders to ensure high-impact deliverables.
You will contribute across the full machine learning lifecycle, including data preparation, feature engineering, model development, and deployment, while helping improve and standardize MLOps practices

Responsibilities

• Develop, train, validate, and deploy machine learning models for real-world applications
• Perform data collection, cleaning, transformation, and feature engineering on structured and unstructured datasets
• Build and maintain scalable data pipelines and ETL processes using Python, SQL, and related tools
• Conduct model selection, evaluation, and tuning, ensuring robustness and avoiding overfitting
• Collaborate with cross-functional teams to move prototypes into production environments
• Contribute to the design and implementation of end-to-end ML workflows (MLOps lifecycle)
• Develop proof-of-concept solutions and iterate toward production-ready systems
• Document methodologies, data sources, models, and system behavior
• Participate in code reviews and promote best practices in development and deployment

Desired Experience and Skills

• Experience with LLM-based solutions such as RAG (Retrieval-Augmented Generation), LangChain, or agent-based systems
• Familiarity with containerization technologies such as Docker
• Exposure to full-stack development concepts (JavaScript, Node.js) for integrating data products into applications
• Experience working with experimentation frameworks and prototyping workflows

Required Experience and Skills

• Strong experience with Python for data science and machine learning
• Proficiency in data manipulation and analysis using Pandas, NumPy
• Solid SQL skills for data extraction and transformation
• Experience building and evaluating ML models using frameworks such as scikit-learn, PyTorch, or Keras
• Understanding of feature engineering, model validation techniques, and performance metrics
• Experience building data pipelines and ETL processes
• Ability to translate business or engineering problems into data science solutions

What We Value

• Passion for building practical, deployable AI solutions
• Ownership mindset from prototype to production
• Curiosity and willingness to learn new tools and techniques
• Collaboration and transparency across teams