Overview

Applied Artificial Intelligence Data Scientist

Responsibilities

• Design, build, and deploy AI-powered solutions leveraging Large Language Models (LLMs), Generative AI, Machine Learning, Predictive Analytics, and AI Agents.
• Develop scalable AI solutions that automate quality analysis, issue detection, root-cause investigation, reporting, forecasting, and decision support.
• Build intelligent copilots and AI assistants that improve productivity for quality engineers, analysts, and program managers.
• Develop Retrieval-Augmented Generation (RAG) solutions leveraging quality documentation, knowledge repositories, and operational data.
• Develop predictive models for quality risk detection, intervention forecasting, anomaly detection, reliability analysis, and failure prediction.
• Create AI-driven early warning systems by integrating quality, service, telemetry, customer, warranty, and operational signals.
• Identify quality trends, emerging risks, and performance drivers through advanced analytics and machine learning.
• Support the organization's journey from reactive issue management to predictive quality management.
• Analyze large-scale structured, semi-structured, and unstructured datasets to generate actionable insights.
• Develop data pipelines, feature engineering frameworks, and analytical workflows supporting AI initiatives.
• Consolidate data across multiple enterprise sources to create integrated quality intelligence platforms.
• Build statistical models, forecasting solutions, and advanced analytics frameworks to support business planning and operational excellence.
• Identify opportunities to automate manual quality processes through AI and intelligent automation.
• Lead AI pilot programs and proof-of-concepts from ideation through deployment.
• Establish reusable AI frameworks, templates, and best practices across the Quality organization.
• Drive adoption of AI technologies through training, enablement, and stakeholder engagement.
• Partner with Quality, Engineering, Services, ISX, Data & Analytics, and Business teams to define AI use cases and implementation roadmaps.
• Translate business requirements into technical solutions and clearly communicate findings to both technical and non-technical audiences.
• Present recommendations, insights, and business impact to senior leadership.
• Promote responsible AI practices, governance, and ethical AI adoption.

Qualifications

• This position supports the Quality organization's vision of building AI-enabled capabilities that transform quality management from reactive monitoring to predictive quality intelligence.
• Bachelor's, Master's, or PhD in Computer Science, Data Science, Artificial Intelligence, Machine Learning, Statistics, Engineering, Mathematics, or a related field.
• 3+ years of experience developing AI, Machine Learning, and Data Science solutions.
• Strong proficiency in Python and modern AI/ML frameworks.
• Experience developing predictive models, machine learning solutions, and data science workflows.
• Experience working with large-scale enterprise datasets using SQL, Spark, Databricks, or equivalent technologies.
• Understanding of statistical modeling, experimentation, model evaluation, and validation methodologies.
• Ability to work effectively in highly collaborative and geographically distributed teams.
• Strong problem-solving, analytical, and communication skills.
• 5+ years of experience developing enterprise AI solutions.
• Experience with Azure AI Services, Azure OpenAI, AI Foundry, Azure Machine Learning, AWS, or GCP.
• Experience with:
• Generative AI
• Large Language Models (LLMs)
• Retrieval-Augmented Generation (RAG)
• Prompt Engineering
• AI Agents and Multi-Agent Systems
• Knowledge Graphs
• Experience with Power BI, Fabric, Databricks, or large-scale analytics platforms.
• Familiarity with MLOps, model monitoring, model deployment, and AI governance.
• Experience in Quality Management, Reliability Engineering, Manufacturing Analytics, Customer Support Analytics, Warranty Analytics, or Service Operations.
• Experience with Microsoft Copilot Studio, Power Platform, and enterprise automation platforms.
• Delivers AI solutions that create measurable improvements in quality outcomes, productivity, and operational efficiency.
• Builds predictive quality intelligence capabilities that enable earlier detection of risks and emerging issues.
• Develops scalable AI copilots, analytics solutions, and intelligent automation capabilities adopted across the Quality organization.
• Successfully translates business challenges into deployed AI solutions that drive business impact.
• Accelerates AI adoption and enables teams to use AI as part of their daily workflows.
• Establishes reusable AI frameworks, standards, and best practices that can scale across portfolios and regions.
• Demonstrates strong ownership, innovation, and execution while collaborating effectively across organizations.
• Applied AI & Machine Learning