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Data Science Machine Learning Jobs in Texas (NOW HIRING)

Confie

Machine Learning Engineer

Addison, TX · On-site +1

$110K - $130K/yr

... Scientists, Data Engineers, and Data Architects on production systems and applications Stay up-to-date with industry trends and advancements in artificial intelligence/machine learning On call ...

Siri InfoSolutions Inc

Data Engineer

San Antonio, TX · On-site

$89K/yr

Automate data workflows, model deployment, and reporting processes using scripting, CI/CD tools, and cloud-based technologies. • Research and evaluate emerging data science, machine learning, and ...

Micron

Staff Data Scientist

Austin, TX

... data science, machine learning, or advanced analytics roles. * Strongproficiencyin Python and SQL, with hands-on experience developing, testing, and deploying predictive or statistical models on ...

Toyota

Principal Data Scientist

Plano, TX

Who we're looking for Toyota's OneTech Data Science and Machine Learning Department is looking for a passionate and highly motivated Principal Data Scientist. The primary responsibility of this role ...

Copart, Inc.

Data Science Analyst

Dallas, TX · On-site

Copart is seeking a Data Science Senior Analyst to help provide vision and commitment to key Copart ... Apply various machine learning techniques to build predictive models. * Continually engage in ...

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All JobsData Science Machine Learning JobsData Science Machine Learning Jobs in Texas

Data Science Machine Learning information

See Texas salary details

$34.9K

$114.3K

$183.1K

How much do data science machine learning jobs pay per year?

As of Jun 29, 2026, the average yearly pay for data science machine learning in Texas is $114,350.00, according to ZipRecruiter salary data. Most workers in this role earn between $91,800.00 and $126,700.00 per year, depending on experience, location, and employer.

Which has more salary, CS or AI?

Data Science and Machine Learning roles in AI generally have higher salaries than traditional computer science positions due to specialized skills in deep learning, neural networks, and advanced algorithms. AI roles often require expertise in programming languages like Python and frameworks such as TensorFlow, which are highly valued in the job market. Salaries vary by experience, location, and industry, but AI-focused positions tend to offer higher compensation on average.

What are the key skills and qualifications needed to thrive as a Data Science Machine Learning professional, and why are they important?

To thrive as a Data Science Machine Learning professional, you need a strong background in statistics, programming (usually Python or R), and a solid understanding of machine learning algorithms, often supported by a degree in computer science, mathematics, or a related field. Familiarity with tools like TensorFlow, scikit-learn, SQL databases, and cloud platforms, as well as certifications such as AWS Certified Machine Learning, are typically valuable. Critical thinking, problem-solving, and effective communication are vital soft skills for interpreting data and collaborating with stakeholders. These skills enable professionals to develop robust models, extract actionable insights, and drive data-driven decision-making in organizations.

What engineers make $500,000?

Senior data science and machine learning engineers with extensive experience, advanced skills in programming, statistical analysis, and deep learning, and often working in high-demand industries or at large tech companies can earn $500,000 or more annually. Compensation typically includes base salary, bonuses, and stock options, especially at executive or specialized levels.

What are some common challenges faced when deploying machine learning models as a Data Science Machine Learning professional?

A frequent challenge in this role is bridging the gap between building accurate models in a controlled environment and deploying them effectively in production systems. Issues such as data drift, model performance degradation, and integration with existing IT infrastructure often arise. Collaboration with engineering and IT teams is crucial to ensure models are scalable, maintainable, and secure. Regular monitoring and updating of deployed models are also essential responsibilities to sustain their value to the business.

What is the difference between Data Science Machine Learning vs Data Analyst?

AspectData Science Machine LearningData Analyst
Required SkillsProgramming (Python, R), statistics, machine learning algorithmsData visualization, SQL, basic statistics
Work EnvironmentDeveloping models, coding, experimenting with algorithmsData reporting, dashboard creation, data cleaning
Industry UsageTech, finance, healthcare, where predictive models are neededBusiness intelligence, marketing, operations

Data Science Machine Learning professionals focus on building predictive models and algorithms using programming and advanced statistics, often working on complex projects. Data Analysts primarily interpret data through visualization and reporting to support business decisions. While both roles require data skills, Data Science Machine Learning involves more technical programming and modeling, whereas Data Analysts focus on data interpretation and presentation.

Do data scientists work with machine learning?

Data scientists often work with machine learning as a core part of their role, developing models to analyze data and make predictions. They use tools like Python, R, and libraries such as scikit-learn or TensorFlow to build and deploy machine learning algorithms. Knowledge of statistics, programming, and data manipulation is essential for this work.

What is data science machine learning?

Data science machine learning refers to the use of algorithms and statistical models to analyze and draw insights from complex data sets. In this field, professionals use machine learning techniques to build predictive models, automate decision-making processes, and uncover patterns in data. Machine learning is a core component of data science, enabling systems to improve their performance over time without being explicitly programmed. Data scientists with machine learning expertise are in high demand across industries like healthcare, finance, and technology.

Which 3 jobs will survive AI?

Data science and machine learning roles are expected to persist as they require complex problem-solving, domain expertise, and creativity that AI tools currently cannot fully replicate. Jobs involving strategic decision-making, ethical considerations, and interpersonal skills, such as data analysts, AI ethics specialists, and AI system trainers, are also likely to remain in demand. Continuous learning and proficiency with AI tools will be essential for these roles to adapt and thrive.
Data Science Machine Learning jobs near you
Infographic showing various Data Science Machine Learning job openings in Texas as of June 2026, with employment types broken down into 60% Full Time, 36% Part Time, 2% Contract, and 2% Nights. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $114,350 per year, or $55 per hour.
Data Scientist - Remote

Data Scientist - Remote

NAVA Software Solutions

Houston, TX • On-site, Remote

Full-time

Posted 26 days ago

Job description

NAVA Software solutions is looking for a Data Scientist
Details:
Data Scientist
Location: Houston TX - Remote is ok
Duration: 12 months
Clients want a data scientist who can develop machine learning models to run forecast scenarios. Also, they want this person to be knowledgeable in AWS Cloud technology
A Data Scientist with physical pipeline experience typically specializes in analyzing and optimizing physical infrastructure pipelines, such as those used in the oil and gas industry or transportation networks. Here are some common job duties associated with this role:
  • Data collection and integration: Data scientists with physical pipeline experience gather data from various sources related to the infrastructure pipelines, such as sensors, SCADA (Supervisory Control and Data Acquisition) systems, or IoT devices. They integrate and consolidate the data for analysis and modeling.
  • Pipeline performance analysis: These professionals analyze the performance of physical pipelines by examining data related to flow rates, pressure levels, temperature, corrosion, and other relevant factors. They use statistical techniques and machine learning algorithms to identify patterns, anomalies, and potential issues that may affect pipeline operations.
  • Predictive modeling and maintenance optimization: Data scientists develop predictive models to forecast pipeline performance and detect potential failures or maintenance needs. They utilize historical data, sensor measurements, and other relevant parameters to train models that can predict future events, such as leaks, blockages, or equipment failures. By identifying critical maintenance requirements in advance, they can optimize maintenance schedules and minimize downtime.
  • Risk assessment and mitigation: Data scientists assess risks associated with physical pipelines, such as environmental hazards, security threats, or regulatory compliance. They develop risk assessment models and analyze the impact of different factors on pipeline safety and integrity. Based on these analyses, they propose mitigation strategies to minimize risks and ensure compliance with safety regulations.
  • Optimization of pipeline operations: Data scientists work on optimizing the operational efficiency of physical pipelines. They analyze data to identify areas of improvement, such as reducing energy consumption, optimizing transportation routes, or improving overall system performance. By applying data-driven approaches and algorithms, they provide recommendations to optimize pipeline operations and maximize efficiency.
  • Visualization and reporting: Data scientists with physical pipeline experience create visualizations, reports, and dashboards to communicate their findings and recommendations effectively. They present complex data in a visually understandable format, allowing stakeholders to make informed decisions regarding pipeline maintenance, operations, and risk management.
  • Collaboration with cross-functional teams: These professionals collaborate with engineers, domain experts, operations personnel, and other stakeholders involved in managing physical pipelines. They work together to understand the specific requirements, constraints, and challenges associated with the infrastructure. Effective communication and teamwork are essential to ensure alignment and successful implementation of data-driven solutions.
  • Continuous improvement and innovation: Data scientists keep up with the latest advancements in data science, machine learning, and pipeline technologies. They explore new methodologies, algorithms, and tools to enhance their skills and propose innovative solutions to address pipeline-related challenges.
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About NAVA Software Solutions

Sourced by ZipRecruiter

NAVA is a strategic partner for companies seeking to develop or customize software and products. Our team of experts leverages cutting-edge technology and deep industry knowledge to provide customized solutions that drive business success. Whether you're looking to improve your operations, increase efficiency, or bring a new product to market, NAVA has the expertise and resources to help you achieve your goals. Trust us to be your partner in software and product development.

Industry

It services

Company size

51 - 200 Employees

Headquarters location

Rocky Hill, CT, US

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