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Senior Data Scientist, Cross-Domain AI

Astspacemobile

Lanham, Maryland, United States (AST - Lanham) Remote permanent

Posted: April 7, 2026

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Quick Summary

We are seeking a Senior Data Scientist, Cross-Domain AI to drive advanced analytics and predictive modeling across a range of applications, including data integration, feature engineering, and model deployment.

Job Description

AST SpaceMobile is building the first and only global cellular broadband network in space to operate directly with standard, unmodified mobile devices based on our extensive IP and patent portfolio and designed for both commercial and government applications. Our engineers and space scientists are on a mission to eliminate the connectivity gaps faced by today’s five billion mobile subscribers and finally bring broadband to the billions who remain unconnected.

Position Overview

We are seeking a Senior Data Scientist, Cross-Domain AI to drive advanced analytics and predictive modeling across a complex, data‑rich environment supporting AI‑enabled and autonomous systems. This role focuses on transforming large, heterogeneous datasets into actionable insights and production‑quality models that inform both machine learning development and executive decision‑making.

The ideal candidate combines rigorous statistical methodology with strong engineering discipline, building robust analytical pipelines and continuously improving models through feedback‑driven training and operational data.

Key Responsibilities

• Perform exploratory data analysis, data cleaning, and data preparation across diverse and complex datasets

• Transform raw, messy, and heterogeneous data (e.g., sensor, telemetry, operational, and production data) into analysis‑ready and ML‑ready formats

• Build predictive and statistical models, including physics‑informed and constraint‑based machine learning approaches

• Design feature engineering frameworks and state‑space representations for reinforcement learning and optimization use cases

• Develop recursive and online training pipelines that continuously retrain and refine models as new data becomes available

• Apply time‑series analysis techniques to identify trends, anomalies, and leading indicators in operational data

• Implement MLOps best practices, including experiment tracking, model versioning, automated validation, and production monitoring

• Build production‑quality analytical pipelines using Python and SQL with robust validation and testing

• Develop causal and statistical models linking process inputs to performance outcomes

• Create analytics, dashboards, and visualizations that translate complex findings into executive‑ready insights

• Collaborate cross‑functionally with engineering, operations, and product teams to support data‑driven decision‑making

Qualifications

Education

• Bachelor’s or Master’s degree in Statistics, Mathematics, Computer Science, Physics, Engineering, or a related quantitative field

• Equivalent practical experience will be considered

Experience

• A minimum of 6 years of professional data science experience

• Demonstrated experience deploying production‑quality analytical or machine learning models

Preferred Qualifications

• Experience working with industrial, scientific, or operational datasets

• Background in advanced modeling techniques such as reinforcement learning, causal inference, or uncertainty quantification

• Experience supporting analytics across complex systems or multi‑disciplinary environments

• Familiarity with recursive, online, or continuous learning workflows

• Advanced academic training or published applied research in a quantitative field

Soft Skills

• Strong interpersonal and collaboration skills across cross‑functional teams

• Excellent written and verbal communication skills

• Ability to clearly present complex analytical findings to technical and non‑technical stakeholders

• Meticulous attention to detail to ensure accuracy and reproducibility of models and analyses

• Strong problem‑solving skills and intellectual curiosity

• Ability to operate effectively in fast‑paced, ambiguous environments

Technology Stack

• Python data science and ML ecosystem (e.g., pandas, NumPy, scikit‑learn, PyTorch or TensorFlow)

• Statistical and probabilistic modeling tools (e.g., statsmodels, Bayesian frameworks)

• SQL and data warehousing technologies

• MLOps and experiment tracking tools (e.g., model registries, monitoring, CI/CD for ML)

• Data visualization and dashboarding tools

Physical Requirements

• Ability to work in a standard office or remote environment

• Ability to use a computer for extended periods

• Ability to participate in meetings and collaborative working sessions

This job description may not be inclusive to the duties and responsibilities listed. Additional tasks may be assigned to the employee from time to time or the scope of the job may change as needed by business demands.

AST SpaceMobile is an Equal Opportunity, at will Employer; employment is governed on the basis of merit, competence and qualifications and will not be influenced in any manner by race, color, religion, gender, national origin/ethnicity, veteran status, disability status, age, sexual orientation, gender identity, marital status, mental or physical disability or any other legally protected status.

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