Senior Data Scientist Job

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 cost
· o 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 processes

AI 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 scalability

AI / NLP / RAG Integration
· Design 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 relevance

o Filtering, slicing, and drill-down analysis
o Customizable chart configurations
Ensure intuitive and scalable self-service user experience

o Data models and lineage documentation
o AI workflow and prompt design documentation
· Agile / SAFe Delivery Execution
· Lead quarterly SAFe Program Increment (PI) planning participation and execution
· o Epics, features, and user stories
· Partner with Scrum Master to:
o AI Program leadership
o Data and platform engineering teams
· Platform Evolution & Integration
· Design 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 KPIs
· Monitor 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 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

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
· o Large language models (Claude)
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

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.