Senior Data Scientist (AI Metrics & Portal)

The Data Scientist AI Metrics & Portal is a technical role responsible for owning the full lifecycle of AI Program metrics including defining architecting implementing operationalizing and continuously improving a standardized AI metrics capability. This role combines data science analytics engineering artificial intelligence and software development to:

• Establish AI Program metricsfrom conceptual definition through technical implementation and ongoing optimization.
• Design build and operate a modern lightweight AI Metrics Hub leveraging Claude Code and other tech stack tools to rapidly develop and maintain an extensible analytics platform.

The Data Scientist will define and operationalize standardized AI metrics architect the supporting data and application layers implement dynamic visualization and AI-driven querying capabilities and ensure continuous evolution of the platform to meet business needs.

• Define standardize and govern AI metrics across adoption utilization performance value cost risk and other categories.
• Architect scalable data models and metrics frameworks to ensure consistency and reuse.
• Implement and operationalize metrics pipelines logic and computation layers.
• Design and build an analytics platform with AI metrics catalog standard/pre-configured AI dashboards and self-service AI dashboards and exploration.
• Implement AI-powered natural language querying and discovery capabilities.
• Maintain and evolve metrics definitions lineage and supporting documentation.
• Deliver iteratively using Agile and SAFe methodologies.
• Enable continuous improvement and future integration with enterprise platforms (e.g. Databricks Collibra).

This role requires a balance of hands-on implementation architecture ownership and delivery leadership with accountability for the end-to-end lifecycle of AI metrics and insights capabilities.

• Own the full lifecycle of AI metrics including:
  • Definition and standardization
  • Architectural design
  • Technical implementation
  • Operational monitoring
  • Continuous improvement
• Define and maintain a comprehensive AI metrics framework including:
  • Adoption utilization engagement
  • Business value and ROI
  • Performance and quality
  • Risk compliance and cost
• Translate business questions into well-defined implementable metrics and models

• Architect scalable reusable metric models including:
  • KPI definitions and calculation logic
  • Dimensional structures and aggregation strategies
• Establish and enforce standards for consistency governance and reuse
• Ensure metrics are designed for extensibility and enterprise integration

• Design and implement metrics computation pipelines and transformations
• Develop and maintain SQL and Python logic for KPI calculation
• Integrate and normalize data from multiple sources (logs APIs databases surveys risk reviews and more)
• Ensure data accuracy consistency and performance optimization
• Implement data quality validation and monitoring processes

• Architect build and maintain the AI Metrics Hub application
• Develop platform components including:
  • Metrics registry (definitions metadata ownership)
  • Dynamic dashboard and visualization engine
  • Config-driven metric execution layer
• Leverage AI-assisted development tools (e.g. Claude Code) to:
  • Accelerate development
  • Generate reusable assets
  • Improve maintainability
• Ensure platform supports rapid iteration and long-term scalability

• Design and implement natural language interfaces for interacting with metrics
• Build and maintain RAG pipelines leveraging:
  • Metric definitions
  • Metadata and contextual information
• Develop prompt engineering strategies and query translation logic
• Enable workflows such as:
  • Ask a question generate query return visualization and explanation
• Continuously improve AI output accuracy usability and relevance

• Design and implement dynamic user-configurable dashboards and visualizations
• Enable:
  • Filtering slicing and drill-down analysis
  • Customizable chart configurations
  • Saved and shareable views
• Deliver export capabilities (PNG CSV PDF)
• Ensure intuitive and scalable self-service user experience

• Develop and maintain:
  • Metrics design specifications
  • Data models and lineage documentation
  • Architecture diagrams
  • AI workflow and prompt design documentation
• Ensure documentation supports transparency governance and reuse

• Lead quarterly SAFe Program Increment (PI) planning participation and execution
• Define and manage:
  • Epics features and user stories
• Partner with Scrum Master to:
  • Plan and execute sprints
  • Maintain and prioritize backlog
• Ensure continuous delivery aligned to program priorities and timelines

• Collaborate with:
  • AI Program leadership
  • Business stakeholders
  • Data and platform engineering teams
• Translate requirements into metrics architecture and implemented solutions
• Communicate outputs clearly to technical and non-technical audiences

• Design and evolve the platform to integrate with:
  • Databricks
  • Collibra
• Identify opportunities to:
  • Enhance automation
  • Improve usability
  • Increase performance and scalability
• Continuously evaluate and adopt emerging AI and analytics capabilities

• Establish and enforce metrics governance processes
• Implement quality controls and validation rules for data and KPIs
• Monitor system usage and platform performance
• Ensure compliance with enterprise data security and governance standards

• Bachelors or Masters degree in Data Science Computer Science Analytics or related field
• 610 years of experience in data science analytics engineering or related field
• Proven experience owning the full lifecycle of metrics/KPI frameworks (definition through implementation)
• Experience building data products analytics platforms or metrics systems
• Experience working in Agile and/or SAFe environments

• Advanced SQL (complex queries performance optimization)
• Strong Python for data processing and analytics
• Deep experience in data modeling and KPI design

• Experience with:
  • Large language models (Claude)
  • Prompt engineering
  • Retrieval-augmented generation (RAG)
  • Vector search
  • Semantic query systems

• Experience building data-driven applications and APIs
• Backend frameworks ( FastAPI or similar)
• Experience with front-end frameworks (React preferred)

• Experience with charting libraries (ECharts Recharts D3) or BI tools
• Strong data visualization and UX principles

• Exposure to Databricks
• Experience with ETL/data pipeline frameworks

• Strong systems thinking and architecture mindset
• Ability to own and execute across the full lifecycle of solutions
• Capability to translate business needs into scalable metrics and data solutions
• Balance between rapid prototyping and maintainable design
• Strong communication and stakeholder engagement skills
• Ownership mindset and comfort operating in ambiguity
• Continuous learning in AI analytics and emerging technologies

Ampcus is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race color religion sex sexual orientation gender identity national origin age protected veterans or individuals with disabilities.