System and Analog Data Scientist

About the role

Altera is looking for a System and Analog Data Scientist to apply advanced analytics and machine learning to the design, verification, and performance optimization of complex system‑on‑chip (SoC) and analog circuits. You will turn large design and test datasets into actionable insights that improve product quality, efficiency, and time‑to‑market.

Key responsibilities

• Develop and maintain data pipelines for collecting, cleaning, and transforming system and analog design and test data.
• Apply statistical analysis, machine learning, and deep learning to detect patterns, anomalies, and correlations in large datasets.
• Create predictive models for circuit performance, reliability, and yield to guide design decisions.
• Design experiments to validate models and measure the impact of data‑driven insights on product development.
• Collaborate with design, verification, and test engineering teams to embed data science solutions into existing workflows.
• Communicate findings through clear visualizations and reports for both technical and non‑technical stakeholders.

Required profile

• BSc, Master’s or Ph.D. in Electrical Engineering, Computer Science, Data Science, Statistics, or a related quantitative field.
• At least 3 years of experience applying data science and machine learning to engineering problems, preferably in the semiconductor industry.
• Strong problem‑solving ability and capacity to translate complex technical challenges into data science projects.
• Excellent communication, collaboration, and presentation skills.

Required skills

• Python (Pandas, NumPy, Scikit‑learn, TensorFlow or PyTorch)
• R
• Statistical modeling and machine‑learning algorithms (regression, classification, clustering, time‑series analysis)
• SQL and data‑warehousing concepts
• Big‑data technologies such as Spark or Hadoop (a plus)
• Understanding of semiconductor design flows, analog circuit fundamentals, and system‑level architectures (highly desirable)