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# Data Science Manager, Forward Deployed

Canonical URL: https://nextmove.md/fde/jobs/829665336014fc63

Govini posts a FDE role to lead deployment teams in Pittsburgh, PA and 1 more with no pay posted. Manages and mentors a forward-deployed team delivering production AI/ML systems for National Security customers, translating client needs into decision-grade models and product features. Read from the employer’s posting on November 8, 2026.

- [Govini](https://nextmove.md/fde/companies/govini)

Posted Jan 30, 2026 by Govini · open on Greenhouse as of Nov 8, 2026

Pay not posted Pittsburgh, PA +1 On-site Full-time

Manages and mentors a forward-deployed team delivering production AI/ML systems for National Security customers, translating client needs into decision-grade models and product features.

- [See roles that fit me](https://nextmove.md/sign-up?from=%2Ffde%2Fjobs%2F829665336014fc63&placement=top)
- [View posting at Govini](https://job-boards.greenhouse.io/air/jobs/4114661009)

Free. Upload your resume once, see why each FDE role fits, and get introduced only when you say so.

## At a glance

### Posted base pay

Not posted

Not stated FDE postings, 50 postings: the middle 50% of posted pay is $151K to $204K, median $180K.

### The work

Leads the deployment team

Balance **Mixed**

Project stages

1. Pre-sales (not covered)
2. Proof of concept (not covered)
3. Implementation
4. Operations (not covered)

Travel **Up to 50%**

- **Coding**: Writes production code

- **Builds for**: External customers

- **Role**: People manager

### What they ask for

Experience

- **3+ years** building and deploying ML or LLM-powered systems in production environments
- **2+ years** leading or mentoring engineering teams in production environments

Background

- **Software engineering**

### Do you meet what the posting states?

The posting asks for 2+ years. How many do you have?

The job is in Pittsburgh, PA · Arlington, VA. Could you work in one of these?

Which of these have you used in production?

Answer each one. Nothing is saved or sent.

[What postings ask for, by background →](https://nextmove.md/fde/paths)

## Conditions

- **Location**: Pittsburgh, PA · Arlington, VA

- **Work arrangement**: On-site

- **Employment type**: Full-time

- **Travel**: Up to 50%

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## The job description from Govini

Posted by Govini on Jan 30, 2026

### 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.**

[View posting at Govini](https://job-boards.greenhouse.io/air/jobs/4114661009)

## About Govini

Govini sells AI-enabled software and data workflows that help DoD and federal teams make faster acquisition, supply-chain, production, and modernization decisions.

Late-stage private · 200–500 people

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