MTS - Data scientist

As a Staff Engineer/ Data Scientist at Micron, you will  

• Develop AI and Data Science based solutions to build state-of-the-art solutions for silicon design verification and firmware validation.
• Identify patterns, anomalies, and inefficiencies in silicon design verification processes and develop solutions to address these gaps.
• Automate data pipelines and develop tools to support regression analysis, bug triaging, and root cause analysis.
• Partner with cross-functional teams to integrate data-driven solutions into EDA tools and verification frameworks.
• Drive technical innovation and culture within the team by participating in generating IP and inspiring team to innovate.
• Participate in end-to-end project scoping and stakeholder discussions to determine technical merit of the idea, vale proposition and resource requirements.
• Interact with subject matter experts to define scope, identify risks, deploy scalable solutions & lead multiple projects execution
• Continuously learn as well as mentor team on recent progress on semiconductor and AI/ML domain.

Key requirements:

• Education: Master’s or PhD in Computer Science, Electrical Engineering, or a related field.
• Experience: 8+ years in data science and machine learning with at least 2 years in semiconductor verification environment

• Technical Skills
  • In-depth understanding of Statistics, classical ML and deep learning, and the mathematics and formulation behind these algorithms.
  • Well versed with text processing, various methodologies in data embedding, NLP techniques and recent advancements in GenAI and LLMs.
  • Hands-on experience with optimization and reinforcement learning based algorithms.
  • Solid understanding of data engineering pipeline for deployment and MLOps.
  • Proficiency in programming languages such as Python, R, and SQL. Experience with machine learning frameworks (e.g., TensorFlow, PyTorch) and data visualization tools (e.g., Tableau, Power BI).
  • Strong understanding of digital design and verification concepts (e.g., RTL, UVM, coverage metrics, simulation).
  • Experience with EDA tools (e.g., Synopsys VCS, Cadence Xcelium, Mentor Questa) and verification flows is a great plus.
• Preferred Qualifications:
  • Knowledge of hardware description languages (Verilog/SystemVerilog).
  • Experience with CI/CD pipelines and MLOps practices.
  • Patents or publications in relevant fields.