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

Job Type: Full-Time, Permanent