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About This Role
- We are hiring a Data Scientist to partner with functional validation (FV) engineers and technologists to accelerate pre and post silicon validation through data driven methods.
- You will design and deploy machine learning algorithms and generative AI-augmented analytics pipelines across bench, lab, and fleet data to improve debug efficiency, coverage quality, and execution predictability.
- Key Responsibilities Lead AI/ML strategy for Post Silicon (post Si) validation by defining technical direction, model architectures, and data foundations that scale across products and sites.
- Architect and drive end to end AI systems: data pipelines, feature stores, training workflows, inference services, and MLOps governance.
- Develop and deploy advanced AI models (e.g., transformers for time series/logs, anomaly detection, root cause prediction, clustering) to accelerate debug and reduce TTR.
- Apply LLMs and RAG to automate triage, summarize complex logs, and recommend next debug steps using historical knowledge.
- Partner with validation, design, FW/BIOS, ATE, and product engineering teams to influence debug methodology and integrate AI insights into execution workflows.
- Lead experimentation frameworks (DOE, A/B tests) to quantify the impact of test content, AI triage systems, and operational improvements.
- Contributed to lean, applied AI algorithms that improved validation efficiency and accelerated development for our next generation, leadership client products.
- Ensure compliance with Intel data governance, reproducibility, and MLOps hygiene best practices.
Requirements
- Degree in Data Science, with at least 3 years of working experience on Data Science or AI Coding Experience.
- Master/PHD in Data Science, Computer Science, Electrical and Electronics/Computer Engineering, Statistics, or related technical field.
- Strong proficiency in Python, ML frameworks (PyTorch/TensorFlow), and SQL.
- Demonstrated experience designing production grade ML systems (pipelines, training, deployment, monitoring).
- Solid grounding in statistics, time series analysis, experiment design, and algorithmic decision making.
- Proficiency in software engineering practices: Git, testing, CI/CD, packaging, API design, cloud/on prem data stacks.
- Proven ability to drive cross team technical alignment, communicate clearly, and influence technical partners.
- Preferred Qualifications Experience with pre and post silicon validation/lab environments, hardware telemetry, and debug artifacts; familiarity with functional validation workflows and KPIs.
- Expertise with transformer-based models, LLM fine tuning, RAG pipelines, or domain specific model adaptation (LoRA/PEFT).
- Strong background in anomaly detection, root cause modeling, graph ML, or large-scale triage automation.
- Hands on with distributed compute (Spark/PySpark), MLOps frameworks (MLflow, model registry), and containerization (Docker).
- Ability to apply GenAI methods (search, summarization, triage) in debug or validation workflows.
- Experience building dashboards (Power BI/Tableau) and designing systems for scalable cross product reuse.
- Familiarity with synthetic data generation, bias/quality checks, and model interpretability (e.g SHAP).
Sourced directly from Intel’s career page
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712 positions
Job ID
/job/Malaysia-Penang/Data-Scientist_JR0280624-1
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