Data Scientist

Job Details: Job Description: 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. Qualifications: Minimum Qualifications 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). Job Type: Experienced Hire Shift: Shift 1 (Malaysia) Primary Location: Malaysia, Penang Additional Locations: Business group: The Silicon Engineering Group (SIG) is a worldwide organization focused on the development and integration of SOCs, Cores, and critical IPs from architecture to manufacturing readiness that power Intel’s leadership products. This business group leverages an incomparable mix of experts with different backgrounds, cultures, perspectives, and experiences to unleash the most innovative, amazing, and exciting computing experiences. Posting Statement: All qualified applicants will receive consideration for employment without regard to race, color, religion, religious creed, sex, national o ... (truncated, view full listing at source)