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Senior Data ScientistAmpcus • Chantilly, Virginia, United States
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Senior Data Scientist

Ampcus
  • US
    Chantilly, Virginia, United States
  • US
    Chantilly, Virginia, United States
Jetzt Bewerben

Über

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
  • Chantilly, Virginia, United States

Sprachkenntnisse

  • English
Hinweis für Nutzer

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