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Aerospace Data Science Jobs in Texas (NOW HIRING)

Qorvo, Inc.

Staff Data Science Engineer

Richardson, TX · On-site

... aerospace/defense. Visit www.qorvo.com to learn how our innovative team is helping connect, protect and power our planet. Role Summary We are looking for a Staff Data Science Engineer to lead the ...

Qorvo, Inc.

Data Engineer

Richardson, TX · On-site

$103K - $124K/yr

... and aerospace/defense. Visit www.qorvo.com to learn how our innovative team is helping connect ... This role sits at the intersection of data science, data engineering, analytics engineering, and AI ...

Qorvo, Inc.

Data Engineer

Richardson, TX

$103K - $124K/yr

... and aerospace/defense. Visit www.qorvo.com to learn how our innovative team is helping connect ... This role sits at the intersection of data science, data engineering, analytics engineering, and AI ...

Aerospace Corporation

$59 - $78.50/hr

... data science, Artificial Intelligence, process improvement, and software development to deliver ... Aerospace's Cyber Security and Advanced Platforms Subdivision is seeking a Secure Hybrid Cloud ...

Firefly Aerospace

GNC Engineer II

Cedar Park, TX · On-site

... computer science, or related field * 2-5 years of professional experience in aerospace guidance ... Experience with Python for scripting, data analysis, and visualization * Familiarity with C++ for ...

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Aerospace Data Science information

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$20.7K

$97.3K

$180.3K

How much do aerospace data science jobs pay per year?

As of Aug 3, 2026, the average yearly pay for aerospace data science in Texas is $97,283.00, according to ZipRecruiter salary data. Most workers in this role earn between $50,127.00 and $135,872.00 per year, depending on experience, location, and employer.

What types of projects do aerospace data science professionals typically work on?

As an Aerospace Data Science professional, you may work on a wide range of projects including optimizing flight operations, predicting maintenance needs through data analytics, analyzing sensor data from aircraft or spacecraft, and enhancing safety or performance using machine learning models. You'll often collaborate with engineers, pilots, and software developers to translate large datasets into actionable insights. Depending on the organization, your work could involve designing and implementing algorithms, automating processes, or improving simulation accuracy for research and development. This dynamic role offers the opportunity to contribute to technology advancements and directly impact operational efficiency and safety in the aerospace sector.

What are the key skills and qualifications needed to thrive in aerospace data science, and why are they important?

Aerospace Data Science professionals require strong analytical skills, experience with statistical modeling, machine learning, and a solid background in aerospace engineering or a related technical field. Proficiency in programming languages such as Python or MATLAB, familiarity with data visualization tools, and knowledge of aerospace data systems like flight data recorders or simulation platforms are highly valued. Excellent problem-solving abilities, clear communication, and the ability to collaborate across multidisciplinary teams set candidates apart. These competencies enable effective interpretation of complex aerospace data, drive innovation, and support critical decision-making in a highly technical industry.

What is an aerospace data science?

An Aerospace Data Science job involves applying data science techniques to analyze, interpret, and optimize aerospace systems. Professionals in this field work with large datasets from satellites, flight sensors, simulations, and maintenance logs to improve aircraft performance, safety, and efficiency. They use machine learning, statistical modeling, and AI to support decision-making in areas like predictive maintenance, navigation, and space exploration. The role requires expertise in both aerospace engineering principles and data science methodologies.

What are the most commonly searched types of Aerospace Data Science jobs in Texas? The most popular types of Aerospace Data Science jobs in Texas are:
What are popular job titles related to Aerospace Data Science jobs in Texas? For Aerospace Data Science jobs in Texas, the most frequently searched job titles are:
What job categories do people searching Aerospace Data Science jobs in Texas look for? The top searched job categories for Aerospace Data Science jobs in Texas are:
What cities in Texas are hiring for Aerospace Data Science jobs? Cities in Texas with the most Aerospace Data Science job openings:
Aerospace Data Science jobs near you
Infographic showing various Aerospace Data Science job openings in Texas as of July 2026, with employment types broken down into 90% Full Time, 5% Part Time, 3% Contract, and 2% Nights. Highlights an 94% Physical, 3% Hybrid, and 3% Remote job distribution, with an average salary of $97,283 per year, or $46.8 per hour.

Staff Data Science Engineer

Qorvo, Inc.

Richardson, TX • On-site

Full-time

Re-posted 15 days ago

Qorvo rating

8.3

Company rating: 8.3 out of 10

Based on 21 frontline employees who took The Breakroom Quiz

Job description

Qorvo (Nasdaq: QRVO) supplies innovative semiconductor solutions that make a better world possible. We combine product and technology leadership, systems-level expertise and global manufacturing scale to quickly solve our customers' most complex technical challenges. Qorvo serves multiple high-growth segments of large global markets, including consumer electronics, smart home/IoT, automotive, EVs, battery-powered appliances, network infrastructure, healthcare and aerospace/defense. Visit www.qorvo.com to learn how our innovative team is helping connect, protect and power our planet.
Role Summary
We are looking for a Staff Data Science Engineer to lead the design and delivery of scalable data science, machine learning, and analytics solutions that create measurable business value across the enterprise. This role sits at the intersection of data science, data engineering, analytics engineering, and AI productization. The right person will pair strong technical depth with practical business judgment, helping turn complex data into decisions, tools, and systems that improve operations, reduce cost, accelerate insight, and scale AI adoption.
This is a senior individual contributor role for someone who can operate as a technical leader across functions, influence stakeholders from engineers to executives, and build robust solutions in environments where data quality, governance, speed, and return on investment all matter.
In the current integration environment, this role must also work effectively within approved collaboration and information-sharing processes, including formal handling of cross-company meetings, data requests, documentation, and CSI-sensitive workflows described in the Project Comet guidance.
What You'll Do
  • Lead the architecture and implementation of production-grade data science and machine learning solutions, from problem framing through deployment and adoption.
  • Build scalable data products, models, and decision-support tools using statistical methods, machine learning, optimization, and modern analytics engineering practices.
  • Partner with business leaders, engineering, IT, manufacturing, quality, finance, and other cross-functional teams to identify high-value opportunities and prioritize work with clear business impact.
  • Translate ambiguous business problems into structured analytical approaches, measurable success criteria, and deliverable roadmaps.
  • Design and maintain reliable data pipelines, feature pipelines, experimentation frameworks, and model monitoring practices.
  • Drive the responsible use of AI across the organization by developing reusable frameworks, templates, evaluation approaches, and best practices for enterprise adoption.
  • Serve as a technical mentor to data scientists, analysts, and engineers; raise the bar on coding, experimentation, documentation, and stakeholder communication.
  • Create executive-ready narratives, visualizations, and recommendations that connect technical findings to business outcomes.
  • Partner with data platform and governance teams to ensure solutions meet requirements for security, compliance, and maintainability.
  • Help shape standards for model lifecycle management, MLOps, analytics engineering, and AI solution delivery.
  • Contribute to integration planning and enterprise analytics initiatives while following approved protocols for meetings, shared materials, data requests, and CSI/non-CSI handling where applicable. Project Comet guidance requires legally approved agendas for certain new cross-company meetings, use of the Data Request List for shared data, and routing potentially sensitive data through the appropriate review path or clean room process.

What Success Looks Like
  • You deliver analytics and AI solutions that produce measurable operational or financial impact.
  • You help the team focus on high-return opportunities that leadership can easily justify and support.
  • You raise technical quality while also improving speed, reuse, and maintainability.
  • You make data science more accessible to the business through better tools, communication, and enablement.
  • You influence decisions well beyond your direct project work.
  • You help the organization use data and AI more effectively without compromising governance, security, or compliance.

Required Qualifications
  • Bachelor's degree in Data Science, Computer Science, Statistics, Engineering, Applied Mathematics, or a related technical field.
  • 8+ years of experience in data science, machine learning, analytics engineering, or data platform development, including experience delivering business-facing solutions in production.
  • Strong programming skills in Python and SQL.
  • Deep experience with statistical analysis, machine learning, feature engineering, model evaluation, and experimental design.
  • Strong experience building data pipelines and working with modern data platforms and cloud analytics ecosystems.
  • Demonstrated ability to own ambiguous, high-impact problems and drive them through to adoption.
  • Experience partnering with senior stakeholders and influencing decisions across technical and non-technical groups.
  • Strong written and verbal communication skills, including the ability to explain complex concepts clearly to executives and business partners.
  • Proven ability to mentor others and lead technically without direct authority.

Preferred Qualifications
  • Advanced degree in a quantitative or technical field.
  • Experience in semiconductor, manufacturing, operations, supply chain, quality, or related industrial domains.
  • Experience building and operationalizing AI/ML solutions at enterprise scale.
  • Experience with MLOps, model monitoring, and deployment workflows.
  • Experience with Databricks, Spark, orchestration tools, BI platforms, and modern software engineering practices.
  • Familiarity with secure data environments and regulated data handling.
  • Experience working in environments that require balancing innovation with compliance, governance, and business urgency.
  • Exposure to enterprise AI enablement, internal tooling, or organization-wide adoption programs.

Technical Skills
  • Python, SQL
  • Machine learning, statistics, optimization, experimentation
  • Data modeling, ETL/ELT, analytics engineering
  • Cloud and distributed data platforms
  • BI and visualization tools
  • Git-based development workflows and production-quality software practices
  • MLOps and model lifecycle management
  • Data governance, documentation, and reproducibility

Leadership Expectations
  • Acts like an owner and focuses on business value, not just technical elegance.
  • Brings an abundance mindset and collaborates across organizational boundaries.
  • Balances strategic thinking with hands-on execution.
  • Pushes for clarity, rigor, and practical outcomes.
  • Elevates the team through mentorship, standards, and example.
  • Exercises strong judgment around sensitive data, stakeholder alignment, and enterprise constraints.

Sample Responsibilities by Problem Type
  • Build predictive and optimization models that improve yield, quality, throughput, cost, or planning.
  • Develop AI-enabled tools that scale analyst and engineer productivity.
  • Create reusable data products that standardize metrics, reduce manual effort, and improve decision speed.
  • Lead diagnostic and exploratory analyses on complex manufacturing, product, or enterprise datasets.
  • Establish frameworks for model governance, evaluation, and business adoption.

This position is not eligible for visa sponsorship by the Company.
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MAKE A DIFFERENCE AT QORVO
We are Qorvo. We do more than create innovative RF and Power solutions for the mobile, defense and infrastructure markets - we are a place to innovate and shape the future of wireless communications. It starts with our employees. As a unified global team, we bring a commitment to excellence, growth and a passion for creating what's next. Explore the possibilities with us.
We are an Equal Employment Opportunity (EEO) employer and welcome all qualified applicants. Applicants will receive fair and impartial consideration without regard to any characteristics protected by applicable law, including race, color, religion, sex (as defined by law), national origin, age, military or veteran status, genetic information, or disability.
Qorvo is an E-Verify Employer. For more information, please see the Right to Work and E-Verify Participation posters.

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