Principal Engineer, Storage Data Science and Analytics

Principal Engineer, Storage Data Science and Analytics

Experience: 12–20 Years

Role Overview

As a Principal Engineer – Data Science & Analytics, you will serve as a senior technical authority at the intersection of Data Science, Machine Learning, GenAI, and Big Data Analytics. You will drive architectural strategy to extract, analyze, and operationalize intelligence from large-scale telemetry data generated by enterprise file, block, and object storage systems.

Key Responsibilities

• Lead architecture and technical strategy for ML, GenAI, and advanced analytics solutions.
• Analyze large-scale storage telemetry and operational data to generate actionable intelligence.
• Build solutions for AIOps, predictive infrastructure failure, anomaly detection, and predictive data management.
• Develop ML/AI models for storage optimization, deduplication, capacity planning, and QoS tuning.
• Design scalable data pipelines and analytics platforms for high-volume telemetry.
• Drive end-to-end AI/ML solutions from experimentation through production deployment.
• Collaborate with engineering, product, and infrastructure teams to translate business and technical challenges into scalable AI solutions.
• Provide technical leadership, architecture guidance, and mentorship to data science and engineering teams.Required Skills
• 1 2+ years of experience in Data Science, ML, AI, Analytics, or related engineering domains.
• Strong expertise in Machine Learning, Deep Learning, GenAI/LLMs, and statistical modeling.
• Hands-on experience with Big Data, distributed systems, data pipelines, and telemetry analytics.
• Strong Python and experience with ML frameworks such as PyTorch/TensorFlow, Spark, and MLflow.
• Experience with AIOps, anomaly detection, predictive analytics, or infrastructure intelligence.
• Understanding of storage technologies – File, Block, Object Storage, deduplication, capacity management, and QoS.
• Strong expertise in cloud, data architecture, MLOps, and production AI systems.
• Excellent system design, architecture, problem-solving, and technical leadership skills.

Must Have Skills & Technical Proficiency

• Core Data Science & ML: Advanced mastery of machine learning algorithms (time-series forecasting, clustering, anomaly detection, random forests) and deep learning frameworks.
• Programming & Systems: Expert Python/Go-lang programmer. Strong working knowledge of data structures, algorithmic complexity, and multi-threaded/mul-process programming.
• Generative AI: Concrete experience building and fine-tuning LLMs, prompt engineering, and leveraging vector databases.
• Application Platform design and deployment: Experience in building scalable data pipeline design, and deployment in a multi-node environment