System and Analog Data Science
Job Details:
Job Description:
Job Summary
Altera is seeking a System and Analog Data Scientist to apply advanced data analytics and machine learning techniques to optimize the design, verification, and performance of complex system-on-chip (SoC) and analog circuits. This role will leverage data-driven insights to improve product quality, efficiency, and time-to-market.
Job Responsibilities
Develop and implement data collection, cleaning, and transformation pipelines for various system and analog design and test data sources.
Apply statistical analysis, machine learning, and deep learning algorithms to identify patterns, anomalies, and correlations in large datasets.
Create predictive models for circuit performance, reliability, and yield, informing design decisions and reducing design iterations.
Design and execute experiments to validate models and evaluate the impact of data-driven insights on product development.
Collaborate with design, verification, and test engineering teams to integrate data science solutions into existing workflows.
Communicate complex analytical findings to technical and non-technical stakeholders through clear visualizations and reports.
Research and stay current with the latest advancements in data science, machine learning, and semiconductor design methodologies.
Qualifications:
BSc, Master's or Ph.D. in Electrical Engineering, Computer Science, Data Science, Statistics, or a related quantitative field.
Proven experience (3+ years) in applying data science and machine learning techniques to engineering problems, preferably within the semiconductor industry.
Strong proficiency in programming languages such as Python (with libraries like Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch) and R.
Experience with statistical modeling, machine learning algorithms (e.g., regression, classification, clustering, time series analysis), and deep learning architectures.
Familiarity with data warehousing, SQL, and big data technologies (e.g., Spark, Hadoop) is a plus.
Understanding of semiconductor design flows, analog circuit fundamentals, and system-level architectures is highly desirable.
Excellent problem-solving skills, with the ability to translate complex technical challenges into solvable data science problems.
Strong communication, collaboration, and presentation skills.