Description

Job Requisition: Applied AI Data Scientist, AIOps and Scale Products, PMTS/Architect Position Overview We are seeking a highly skilled and experienced Data Scientist for AIOps and Scale Products at the Principal/Architect level to join our innovative team, which focuses on building an AIOps platform for all Salesforce services. This role is crucial for designing, developing, and deploying advanced analytical solutions that drive business strategy and decision-making within an AIOps framework. The ideal candidate will possess a deep understanding of statistical modeling, machine learning, and data engineering principles, with a proven track record of leading complex data science projects from inception to production, specifically within operational intelligence and automation. Responsibilities - Lead the design and implementation of scalable and robust data science architectures for AIOps, focusing on predictive analytics, anomaly detection, and root cause analysis - Develop and deploy advanced machine learning models and algorithms to solve complex business problems related to IT operations, such as incident prediction, performance optimization, and automation - Collaborate with cross-functional teams, including engineers, product managers, and business stakeholders, to define project requirements and deliver impactful AIOps solutions - Provide technical leadership and mentorship to junior data scientists and engineers, fostering expertise in AIOps methodologies - Evaluate and recommend new technologies and methodologies to enhance our AIOps capabilities and operational intelligence - Ensure the integrity, accuracy, and security of operational data used in analytical models for AIOps - Communicate complex analytical findings and insights to both technical and non-technical audiences, particularly in the context of operations and business impact. Qualifications Required -10+ years of experience in data science, with at least 3 years in an architect or lead role - Strong proficiency in Object-oriented programming language, Python, SQL and experience with large-scale data processing technologies - Deep understanding of statistical modeling, machine learning algorithms, and experimental design, with a focus on time-series analysis, anomaly detection, and predictive modeling for operational data - Proven experience in designing and implementing end-to-end data science solutions - Excellent communication and presentation skills Preferred - Experience with cloud platforms (e.g., AWS, GCP, Azure) and MLOps practices in an AIOps context - Familiarity with deep learning frameworks (e.g., TensorFlow, PyTorch) for processing operational logs, metrics, and traces - Experience with real-time data streaming and processing from various application profiling, IT monitoring and observability tools - Good understanding of Systems Engineering, OS, JVM, DB and application performance monitoring concepts