Senior Staff data science engineer

Key Responsibilities

• Develop advanced machine learning, statistical, and optimization models to solve complex business and engineering problems
• Apply end-to-end data science rigor: problem framing, feature engineering, modeling, validation, and impact measurement
• Work with large-scale datasets (e.g., process, yield, test, operational data) to derive actionable insights
• Integrate domain knowledge (semiconductor/NAND processes, constraints, variability) directly into model design and interpretation
• Design and deploy GenAI solutions (LLMs, RAG pipelines, agent-based systems) for engineering and operational use cases
• Leverage enterprise data (documents, logs, process data) to build knowledge-driven systems
• Develop evaluation frameworks to ensure quality, grounding, and reliability of GenAI outputs
• Apply GenAI to enable decision support, automation, and productivity at scale
• Build and scale MLOps pipelines (CI/CD, model registry, monitoring, drift detection)
• Operationalize models into reliable, maintainable production systems
• Partner with cross-functional teams (engineering, manufacturing, product, IT) to translate requirements into solutions
• Mentor engineers and data scientists in advanced modeling, system design, and GenAI capabilities

Qualifications

• Master’s degree in Computer Science, Electrical Engineering, Statistics, Applied Mathematics, or a related quantitative field
• 8–12+ years of experience in AI/ML engineering, data science, or related domains
• Strong expertise in Python and modern ML frameworks (PyTorch, TensorFlow, scikit-learn)
• Statistical modeling, experimentation, and data science methodologies
• Scalable ML system design and production deployment
• Proven track record of delivering production-grade AI/ML systems with measurable business impact

Preferred

• PhD in a relevant field (e.g., Machine Learning, AI, Statistics, Operations Research, Electrical Engineering) with significant industry experience
• Deep experience applying AI/ML in complex, real-world systems (e.g., semiconductor, manufacturing, or large-scale operational environments)
• Hands-on experience with Generative AI / LLM systems (RAG, prompt engineering, evaluation frameworks, fine-tuning)
• MLOps platforms (MLflow, Azure ML, Kubeflow, or equivalent)
• Distributed data platforms (Spark, Databricks)