Senior Data Scientist (AI Metrics & Portal)

Ampcus Incorporated

Chantilly, VA

JOB DETAILS
SKILLS
Adobe Acrobat, Agile Programming Methodologies, Analysis Skills, Application Programming Interface (API), Architectural Design, Artificial Intelligence (AI), Artificial Intelligence (AI) Natural Language, Artificial Intelligence (AI) Programming Languages, Automation, Backlog Prioritization, Business Intelligence Software, Business Services, Communication Skills, Computer Science, Consulting, Continuous Deployment/Delivery, Continuous Improvement, Cross-Functional, Data Analysis, Data Management, Data Modeling, Data Processing, Data Quality, Data Science, Data Visualization, Database Extract Transform and Load (ETL), Documentation, Emerging Technology, Enterprise Protection, Information/Data Security (InfoSec), Interface Programming Languages, Leadership, Maintain Compliance, Metadata, Metrics, Modeling Languages, Natural Language Processing (NLP), Node.js, PNG, Performance Management, Performance Metrics, Performance Tuning/Optimization, Product Development, Productivity Management, Programming Tools, Python Programming/Scripting Language, Quality Monitoring, Query Optimization, Rapid Prototyping, React.js, Reporting Dashboards, Requirements Management, Return on Investment (ROI), Risk, SQL (Structured Query Language), ScrumMaster, Software Administration, Software Development, Sprint Planning, Standards Development, Strategic Planning, System Architecture, Usability Engineering, User Interface/Experience (UI/UX), Web Analytics
LOCATION
Chantilly, VA
POSTED
23 days ago

Ampcus Inc. is a certified global provider of a broad range of Technology and Business consulting services. We are in search of a highly motivated candidate to join our talented Team.

Job Title: Senior Data Scientist (AI Metrics & Portal)
Location: Chantilly VA – 20151

Duration: Long term/ Direct hire

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:

  • Establish AI Program metrics—from conceptual definition through technical implementation and ongoing optimization.
  • 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.

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

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


 

Ampcus is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, protected veterans or individuals with disabilities.

About the Company

A

Ampcus Incorporated

Ampcus Inc is a global technology and business consulting firm specializing in Digital Transforrmation, Big Data, Analytics, Cyber Security, Testing, IV&V, Infrastructure Management and Enterprise Solutions. Ampcus Inc is an SBA 8(a) certified Women and Minority Owned global Provider of broad range of consulting Services. From strategy to execution, our disciplined yet flexible approach starts and ends with our clients. By listening hard and working harder, their goals become our goals. We are an ISO 9000, ISO 20000, ISO 27000 and CMMi Level certified company.

Ampcus consultants have significant business, engineering and technology experience. Our consultants have over 20 years of business experience and an average of over 10 years of engineering and technology experience. This means that the project teams understand how systems work and how the technology impacts the business processes of organizations.

We believe that success of an engagement is determined by strong project management, clear communication and mutual commitment working collaboratively. Our methodology begins by listening to the customer needs, then working with their teams to gain a clear understanding of the requirements, while providing a knowledge transfer of best practices for the organization. As a recognized leader providing customized software services, management and engineering solutions to companies around the world, our ability to deliver is a "granted"​ that makes companies put their trust in us to answer their day-to-day business challenges and put them on a path for greater success. We are the choice for our clients because we look at our clients business from a growth perspective.

Industry: Information Technology and Services

Specialties: Digital Transformation, Big Data and Analytics, Infrastructure Management Services, Testing and IV&V, Cyber Security, Active Directory and E-mail Infrastructure, Project Management, Training, and ERP, CRM. EAI, BI

COMPANY SIZE
500 to 999 employees
INDUSTRY
Staffing/Employment Agencies
WEBSITE
http://www.ampcus.com