Applied Artificial Intelligence Data Scientist

Description -

AI Data Scientist - Quality Intelligence & Transformation

Overview

The Quality organization is seeking an AI Data Scientist to accelerate the transformation of quality operations through Artificial Intelligence, Machine Learning, Predictive Analytics, and Generative AI.

This role will partner with Quality Program Managers (QPMs), Quality Engineers, Data Analysts, Service teams, Product teams, and Technology organizations to design, build, evaluate, and scale AI-powered solutions that improve product quality, customer experience, operational efficiency, and business decision-making.

The ideal candidate combines strong technical expertise in Large Language Models (LLMs), Generative AI, Machine Learning, Predictive Analytics, and Data Science with an understanding of quality, reliability, service, and operational processes. Success in this role requires the ability to identify high-value business problems, translate them into scalable AI solutions, and drive measurable business outcomes.

This position supports the Quality organization's vision of building AI-enabled capabilities that transform quality management from reactive monitoring to predictive quality intelligence.

Key Responsibilities

AI Solution Development

• 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.

Predictive Quality Intelligence

• 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.

Data Science & Analytics

• 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.

AI Transformation & Automation

• 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.

Collaboration & Leadership

• 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.

Required Qualifications

• 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.

Preferred Qualifications

• 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.

What Success Looks Like

A successful AI Data Scientist - Quality Intelligence & Transformation:

• 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.

Core Competencies

• Applied AI & Machine Learning
• Large Language Models (LLMs) & Generative AI
• Predictive Quality Analytics
• Data Science & Statistical Modeling
• Quality Intelligence & Early Warning Systems
• AI Agents & Intelligent Automation
• Experiment Design & Model Evaluation
• Business Problem Solving
• Communication & Executive Storytelling
• Cross-Functional Collaboration
• Innovation & Continuous Improvement
• Ownership & Accountability

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Equal Opportunity Employer (EEO)-

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