Data Scientist Senior - Remote

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. 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. AI Metrics Lifecycle Ownership (Define → Architect → Implement → Operate → Evolve) Own the full lifecycle of AI metrics, including: o Technical implementation o Operational monitoring Define and maintain a comprehensive AI metrics framework, including: o Performance and quality o Risk, compliance, and costo Dimensional structures and aggregation strategies 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 processesAI Metrics Portal Development Architect, build, and maintain the AI Metrics Hub application Develop platform components, including: o Metrics registry (definitions, metadata, ownership) Leverage AI-assisted development tools (e.g., o Generate reusable assets Ensure platform supports rapid iteration and long-term scalabilityAI / NLP / RAG IntegrationDesign and implement natural language interfaces for interacting with metrics Build and maintain RAG pipelines leveraging: o Metadata and contextual information Continuously improve AI output accuracy, usability, and relevanceo Filtering, slicing, and drill-down analysiso Customizable chart configurations Ensure intuitive and scalable self-service user experienceo Data models and lineage documentationo AI workflow and prompt design documentation Agile / SAFe Delivery Execution Lead quarterly SAFe Program Increment (PI) planning participation and executiono Epics, features, and user stories Partner with Scrum Master to: o AI Program leadership o Data and platform engineering teams Platform Evolution & IntegrationDesign and evolve the platform to integrate with: o Databricks o Increase performance and scalability Continuously evaluate and adopt emerging AI and analytics capabilities Governance, Quality & Performance Implement quality controls and validation rules for data and KPIsMonitor system usage and platform performance Ensure compliance with enterprise data, security, and governance standards Bachelor's or Master's degree in Data Science, Computer Science, Analytics, or related field6–10+ 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 Data & Analytics Advanced SQL (complex queries, performance optimization) Strong Python for data processing and analytics Deep experience in data modeling and KPI designAI & Machine Learningo Large language models (Claude) Software Development Experience building data-driven applications and APIsBackend 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 Capability to translate business needs into scalable metrics and data solutions· Continuous learning in AI, analytics, and emerging technologies 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.