Data Scientist IV - AI/ML Operations Engineer

OverviewThis role involves fostering a continuous learning environment by sharing knowledge, resources, and expert advice with colleagues and stakeholders. The individual will build strong relationships across teams, incorporate feedback for performance improvement, and lead by example through technical guidance and impactful recommendations. Adaptability and embracing change are key, including assisting peers with new challenges and processes while promoting effective collaboration to drive business objectives. Key ResponsibilitiesIndependently complete assignments and contribute to business projects by applying domain expertise and creative solutions, encouraging adherence to organizational policies.Work cross-functionally and externally to support sound business decisions, manage priorities, escalate critical issues timely, and track progress.Support planning efforts to meet deadlines, allocate resources, and identify opportunities for continuous improvement by influencing and engaging relevant stakeholders.Create detailed problem statements with clear hypotheses and measurable objectives for impacting clients or customers.Design and build data pipelines to automate data ingestion and transformation from diverse sources and formats, including writing efficient SQL queries and applying advanced database principles.Analyze complex datasets using visualization techniques to uncover patterns, detect anomalies, test hypotheses, and verify assumptions.Process and engineer features for machine learning by leveraging dimensionality reduction, feature importance, and selection techniques.Train and evaluate statistical models using various algorithms and data mining approaches, implementing strategies like cross-validation to prevent overfitting.Deploy, maintain, and monitor models in production environments to ensure reliability and efficiency.Validate model performance using diverse assessment methods and refine models based on feedback and results.Collaborate with internal and external stakeholders to provide data-driven insights, support decision-making, and communicate findings effectively to diverse audiences. Preferred Skills and KnowledgeExperience in MLOps and AI platforms including model deployment and monitoring utilizing tools such as SageMaker, Vertex AI, and MLflow.Familiarity with Site Reliability Engineering (SRE) practices including SLIs/SLOs, error budgets, incident management, runbook creation, and postmortem analysis.Understanding of IT operations frameworks and practices such as ITIL/ITSM, along with proficiency using ticketing systems.Strong communication skills and ability to solve problems in a structured manner. Additional InformationThe role requires someone adaptable who pursues self-development and acts as a change agent within teams, advocating for new processes and supporting collaborative environments for business impact. EligibilityOpen to applicants who have completed any graduate degree. ResponsibilitiesDrive knowledge sharing and foster continuous learning within the team and stakeholders.Deliver independent work and contribute effectively to business projects using domain expertise.Collaborate across functions and with external partners to guide business decisions and solve complex challenges.Develop detailed problem definitions with measurable impacts for client solutions.Build and optimize automated data pipelines and write complex SQL queries.Analyze large datasets to identify trends, anomalies, and validate business assumptions.Engineer and select features for machine learning modeling.Train, validate, and deploy statistical and machine learning models effectively into production.Monitor model performance and adjust based on evaluation feedback.Communicate insights and collaborate with diverse stakeholders to inform decisions. RequirementsExperience with MLOps and AI platforms such as SageMaker, Vertex AI, and MLflow for model deployment and monitoring.Knowledge of Site Reliability Engineering practices, including SLIs/SLOs, error budgets, incident management, and runbook documentation.Understanding of IT operations standards, including ITIL/ITSM, and experience working with ticketing tools.Effective communication skills and structured approach to problem-solving. .