Senior Data Scientist

Summary

• A hands-on data scientist responsible for the full lifecycle of AI metrics—defining, architecting, implementing, and evolving a modern, AI-powered analytics platform and metrics that enables self-service insights across the enterprise.

Position Overview

• 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: (1) Establish AI Program metrics—from conceptual definition through technical implementation and ongoing optimization, and (2) 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.

The role will orchestrate metrics design, platform engineering, and Agile delivery practices to:

• 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.

Required Qualifications

Education & Experience

• Bachelor’s or Master’s degree in Data Science, Computer Science, Analytics, or related field
• 6–10+ 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

Technical Skills

Data & Analytics

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

AI & Machine Learning

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

Software Development

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

Data Visualization

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

Data Platforms (Preferred)

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

Key Competencies

• 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

Key Responsibilities

1. AI Metrics Lifecycle Ownership (Define → Architect → Implement → Operate → Evolve)

• 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

2. Metrics Architecture & Standardization

• 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

3. Metrics Implementation & Data Engineering

• 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

4. AI Metrics Portal Development

• 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

5. AI / NLP / RAG Integration

• 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

6. Visualization & Self-Service Enablement

• 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

7. Documentation & Design Artifacts

• 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

8. Agile / SAFe Delivery Execution

• 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

9. Cross-Functional Collaboration

• 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

10. Platform Evolution & Integration

• 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

11. Governance, Quality & Performance

• 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