Senior Data Scientist

SummaryA 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 OverviewThe 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 & ExperienceBachelor’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 fieldProven experience owning the full lifecycle of metrics/KPI frameworks (definition through implementation)Experience building data products, analytics platforms, or metrics systemsExperience working in Agile and/or SAFe environments Technical Skills Data & AnalyticsAdvanced SQL (complex queries, performance optimization)Strong Python for data processing and analyticsDeep experience in data modeling and KPI design AI & Machine LearningExperience with: Large language models (Claude)Prompt engineeringRetrieval-augmented generation (RAG)Vector searchSemantic query systems Software DevelopmentExperience building data-driven applications and APIsBackend frameworks (Node.js, FastAPI, or similar)Experience with front-end frameworks (React preferred) Data VisualizationExperience with charting libraries (ECharts, Recharts, D3) or BI toolsStrong data visualization and UX principles Data Platforms (Preferred)Exposure to DatabricksExperience with ETL/data pipeline frameworks Key CompetenciesStrong systems thinking and architecture mindsetAbility to own and execute across the full lifecycle of solutionsCapability to translate business needs into scalable metrics and data solutionsBalance between rapid prototyping and maintainable designStrong communication and stakeholder engagement skillsOwnership mindset and comfort operating in ambiguityContinuous 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 standardizationArchitectural designTechnical implementationOperational monitoringContinuous improvementDefine and maintain a comprehensive AI metrics framework, including: Adoption, utilization, engagementBusiness value and ROIPerformance and qualityRisk, compliance, and costTranslate business questions into well-defined, implementable metrics and models 2.

Metrics Architecture & StandardizationArchitect scalable, reusable metric models, including: KPI definitions and calculation logicDimensional structures and aggregation strategiesEstablish and enforce standards for consistency, governance, and reuseEnsure metrics are designed for extensibility and enterprise integration 3.

Metrics Implementation & Data EngineeringDesign and implement metrics computation pipelines and transformationsDevelop and maintain SQL and Python logic for KPI calculationIntegrate and normalize data from multiple sources (logs, APIs, databases, surveys, risk reviews, and more)Ensure data accuracy, consistency, and performance optimizationImplement data quality validation and monitoring processes 4.

AI Metrics Portal DevelopmentArchitect, build, and maintain the AI Metrics Hub applicationDevelop platform components, including: Metrics registry (definitions, metadata, ownership)Dynamic dashboard and visualization engineConfig-driven metric execution layerLeverage AI-assisted development tools (e.g., Claude Code) to: Accelerate developmentGenerate reusable assetsImprove maintainabilityEnsure platform supports rapid iteration and long-term scalability 5.

AI / NLP / RAG IntegrationDesign and implement natural language interfaces for interacting with metricsBuild and maintain RAG pipelines leveraging: Metric definitionsMetadata and contextual informationDevelop prompt engineering strategies and query translation logicEnable workflows such as: “Ask a question generate query return visualization and explanation”Continuously improve AI output accuracy, usability, and relevance 6.

Visualization & Self-Service EnablementDesign and implement dynamic, user-configurable dashboards and visualizationsEnable: Filtering, slicing, and drill-down analysisCustomizable chart configurationsSaved and shareable viewsDeliver export capabilities (PNG, CSV, PDF)Ensure intuitive and scalable self-service user experience 7.

Documentation & Design ArtifactsDevelop and maintain: Metrics design specificationsData models and lineage documentationArchitecture diagramsAI workflow and prompt design documentationEnsure documentation supports transparency, governance, and reuse 8.

Agile / SAFe Delivery ExecutionLead quarterly SAFe Program Increment (PI) planning participation and executionDefine and manage: Epics, features, and user storiesPartner with Scrum Master to: Plan and execute sprintsMaintain and prioritize backlogEnsure continuous delivery aligned to program priorities and timelines 9.

Cross-Functional CollaborationCollaborate with: AI Program leadershipBusiness stakeholdersData and platform engineering teamsTranslate requirements into metrics, architecture, and implemented solutionsCommunicate outputs clearly to technical and non-technical audiences 10.

Platform Evolution & IntegrationDesign and evolve the platform to integrate with: DatabricksCollibraIdentify opportunities to: Enhance automationImprove usabilityIncrease performance and scalabilityContinuously evaluate and adopt emerging AI and analytics capabilities 11.

Governance, Quality & PerformanceEstablish and enforce metrics governance processesImplement quality controls and validation rules for data and KPIsMonitor system usage and platform performanceEnsure compliance with enterprise data, security, and governance standards