Air
Air4 open roles

Data Science Manager, Forward Deployed

Arlington, VA; Pittsburgh, PAOn-siteSeniorFull-time

Posted 4 days ago

The role in short

Leads Air's forward-deployed data science and AI engineering team, building and shipping production ML/LLM systems for national security customers.

Company Description

Air is the leader in Enterprise Readiness. Our mission is to establish readiness as a real-time condition that is continuously achieved. Today, a dangerous Readiness Gap exists between what the front line needs and what is delivered. Our AI-native platform, Air Enterprise Readiness, aligns development, production, delivery, and sustainment into one coordinated execution system for government agencies and industrial suppliers. By revealing true capacity, exposing real constraints, coordinating resources, and executing at the speed of operational demands, the front line gets what it needs to succeed.

Job Description

At Air, the Data Science Manager, Forward Deployed, is responsible for managing the AI model development discipline and team, including AI roadmap development, AI/ML model development, evaluation, and designing models and AI systems for particular use cases for National Security customers. You will collaborate with Product, Sales, and Engineering in a cross-functional capacity to deliver high-quality, decision-grade models and product features to our customers.  You will work with our customers directly to better understand their key challenges. You thrive under pressure and are eager to solve new challenges.  You are passionate about recruiting and developing top data science talent.  You are a technology and data nerd who strives to always deliver the best product to our customers.

In order to do this job well, you will push the boundaries of machine learning, with a focus on delivering clear, direct value to our customers. You will always look for new ways to leverage models to drive new insights and customer value.  You won’t hesitate to identify problems and take the lead on finding the answers.  You are a self-starter who leads by example.  You are a creative, out-of-the-box thinker who shares our passion and obsession with problem-solving.  As part of our team, you must have a strong understanding of the various fundamentals of data science and analysis, and machine learning model development and productionalization. You have a proven track record for implementation and excel at the successful implementation of AI models.

This role is a full-time position located out of our office in Pittsburgh, PA or Arlington, VA office.

This role may require up to 50% travel.

Scope of Responsibilities

  • Lead and mentor a team of forward-deployed data scientists, AI engineers, and MLE engineers, guiding them from ideation to implementation, and fostering a collaborative and productive work environment.
  • Organize complex AI/ML/DS work for multiple clients, spanning science cases such as LLM/GenAI, reinforcement learning, decision science, capability simulation, document understanding, forecast models, and others, across areas like logistics, MRO, supply chains, and others.
  • Establish coding, testing, and AI coding best practices and standards for engineers, driving excellence in software development.
  • Oversee AI development for practical use cases in deployment by nurturing the AI team culture, defining AI/ML tool usage, and applying scientific methods.
  • Oversee the quality of scientists' deliverables, including model delivery, coding, evaluation, and testing, to maintain high standards of science and software.
  • Collaborate closely with client stakeholders, Air's product, forward deployment, and engagement teams to translate business requirements into technical specifications.
  • Continuously seek innovation and process improvement opportunities, finding efficiencies in all assigned tasks to drive the team's success.
  • Work with AI platform teams to ensure generalization of AI cases built for particular clients into platform and product features.
  • Organize and lead processes for the science team, ensuring efficient project execution and alignment with business goals.

Qualifications

  • U.S. Citizenship is required

Required Skills:

  • Bachelor's, Master’s, or Doctorate in Computer Science, Computer Engineering, Data Science, or a related field
  • Minimum 3 years of experience building and deploying ML or LLM-powered systems in production environments
  • Minimum 2  years of experience leading or mentoring engineering teams in production environments
  • Practical experience in building, developing, and productionizing machine learning systems
  • Advanced software skills in Python
  • Experience with common LLM algorithms and implementations
  • Hands-on experience with AWS and cloud infrastructure
  • A strong desire to learn and investigate new technologies
  • Familiarity with Git source control management
  • Ability to work collaboratively with little supervision
  • A burning desire to work in a challenging fast-paced tech environment

Desired Skills:

  • Current possession of a U.S. security clearance, or the ability to obtain one with our sponsorship
  • Experience in or exposure to the nuances of a startup or other entrepreneurial environment
  • Experience with Claude Code and GenAI coding best practices.

We firmly believe that past performance is the best indicator of future performance.  If you thrive while building solutions to complex problems, are a self-starter, and are passionate about making an impact in global security, we’re eager to hear from you.

Air is an Equal Opportunity Employer.  All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability and protected veterans status or any other characteristic protected by law.

Prep for this interview

3 questions likely to come up at Air, each matched to a line in this posting.

  1. 1Discovery and scoping
    They wrote: “Collaborate closely with client stakeholders, Air's product, forward deployment, and engagement teams to translate business requirements into technical specifications.”

    Tell me about a time the customer asked for one thing but needed another.

    How to answer

    Why they ask: Good FDEs spot when a request is a guess at a solution. They want proof you can redirect a customer without losing their trust.

    1. What they asked for, and the detail that made you doubt it
    2. How you tested the doubt (shadowed users, looked at logs, ran a quick prototype)
    3. How you made the case to the customer
    4. What shipped and what changed for them

    Tip: Pick a story where you checked your hunch with data or users before pushing back.

  2. 2Cloud, deployment and security
    They wrote: “Current possession of a U.S. security clearance, or the ability to obtain one with our sponsorship”

    The customer's security team sends a 200-question review. How do you handle it?

    How to answer

    Why they ask: Security reviews often block deals. FDEs help get through them.

    1. Start from your company's standard answers (SOC 2 report, past reviews)
    2. Answer clearly; don't oversell
    3. Bring in your security team for anything you're unsure of
    4. Keep the new answers for the next customer

    Tip: Reuse past answers, flag honest gaps, and offer a call for the hard questions.

  3. 3LLMs, RAG and agents
    They wrote: “Experience with common LLM algorithms and implementations”

    A customer wants a chatbot over their internal documents. How do you build it?

    How to answer

    Why they ask: RAG is the most common first project. They want the whole path, from messy files to answers people trust.

    1. Collect and clean the documents; handle permissions per user
    2. Split them into chunks, embed, and store with source links
    3. Retrieve, then answer with citations; say “I don't know” when retrieval is weak
    4. Build a test set of real questions with expected answers before launch

    Tip: Spend more time on the data and on testing than on the model.

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