Data Science Engineer

Sandisk understands how people and businesses consume data and we relentlessly innovate to deliver solutions that enable today’s needs and tomorrow’s next big ideas. With a rich history of groundbreaking innovations in Flash and advanced memory technologies, our solutions have become the beating heart of the digital world we’re living in and that we have the power to shape.

• 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.
• Build knowledge-driven systems leveraging enterprise data (documents, logs, process data).
• Develop evaluation frameworks to ensure quality, grounding, and reliability of GenAI outputs.
• Apply GenAI to enable decision support, automation, and productivity at scale.
• Architect production-grade AI/ML systems, including data pipelines, feature engineering frameworks, model training and experimentation platforms, real-time and batch inference systems.
• Design systems that balance accuracy, scalability, latency, and cost efficiency.
• Establish patterns for integrating AI into existing enterprise platforms and workflows.
• Build and scale MLOps pipelines (CI/CD, model registry, monitoring, drift detection).
• Ensure reproducibility, observability, and governance of AI systems.
• Lead efforts to 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.

• Master’s degree in Computer Science, Electrical Engineering, Statistics, Applied Mathematics, or a related quantitative field.
• 8–12+ years of experience in machine learning engineering, data science methodologies, and large-scale AI systems.
• Deep expertise in machine learning engineering, data science methodologies, and large-scale AI systems.
• Strong domain intuition in semiconductor/NAND environments.

• Full-time position.
• Salary Range: 172,783.00-286,178.00 USD per year.