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Computational Data Science Jobs in Texas (NOW HIRING)

... data. Skills & Qualifications * PhD from a recognized university in Engineering, Applied Mathematics, Geoscience, Computational Science, or a closely related field. * Experience in developing ...

... data. Skills & Qualifications * PhD from a recognized university in Engineering, Applied Mathematics, Geoscience, Computational Science, or a closely related field. * Experience in developing ...

Data Scientist Level 3

Boerne, TX · On-site

$92K - $126K/yr

Foundations: (Mathematical, Computational, Statistical) * Data Processing: (Data management and ... Data science * Advanced analytical algorithms * Programming (skill in at least one high-level ...

Foundations: (Mathematical, Computational, Statistical) * Data Processing: (Data management and ... Data science * Advanced analytical algorithms * Programming (skill in at least one high-level ...

Bachlelor's degree in data science, Computer Science, Engineering, Statistics, Mathematics ... Computational Programming with Python including packages such as Pandas, scikit-learn, Pytorch ...

Education Bachlelor's degree in data science, Computer Science, Engineering, Statistics ... Experience deploying models via APIs, batch pipelines, or streaming architecture Computational ...

Bachlelor's degree in data science, Computer Science, Engineering, Statistics, Mathematics ... Computational Programming with Python including packages such as Pandas, scikit-learn, Pytorch ...

Apply a range of data science techniques and tools combined with subject matter expertise to solve ... Implement models that comply with evaluations of the computational demands, accuracy, and ...

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Computational Data Science information

See Texas salary details

$15

$52

$75

How much do computational data science jobs pay per hour?

As of Jun 14, 2026, the average hourly pay for computational data science in Texas is $52.93, according to ZipRecruiter salary data. Most workers in this role earn between $43.46 and $62.69 per hour, depending on experience, location, and employer.

What does a computational data scientist do?

A computational data scientist analyzes large datasets using programming languages like Python or R, develops algorithms, and applies statistical models to extract insights and solve complex problems. They often work with machine learning tools and require strong analytical skills to support data-driven decision-making in organizations.

What is the difference between Computational Data Science vs Data Analyst?

AspectComputational Data ScienceData Analyst
Required CredentialsTypically requires a degree in Computer Science, Data Science, or related fields; often includes programming certificationsUsually requires a degree in Statistics, Business, or related fields; may include basic data analysis certifications
Work EnvironmentInvolves programming, modeling, and developing algorithms; often in tech or research settingsFocuses on interpreting data, creating reports, and supporting decision-making; in business or corporate environments
Employer & Industry UsageUsed in tech companies, research institutions, and industries requiring advanced modelingCommon in finance, marketing, healthcare, and business sectors

Computational Data Science involves advanced programming, algorithm development, and modeling, often in technical environments. Data Analysts focus on interpreting data, generating reports, and supporting business decisions. While both roles work with data, Computational Data Scientists typically require stronger programming skills and work on building models, whereas Data Analysts focus on data interpretation and visualization.

What is Computational Data Science?

Computational Data Science is an interdisciplinary field that combines computer science, statistics, and domain knowledge to extract insights and knowledge from complex data sets using computational techniques. Professionals in this field use algorithms, machine learning, and advanced analytics to solve real-world problems by processing and interpreting large volumes of data. The work often involves programming, data modeling, and visualization, making it crucial in industries such as healthcare, finance, and technology. Computational Data Scientists help organizations make data-driven decisions and innovate through predictive modeling and data analysis.

Is 40 too late for data science?

Computational Data Science is a field where individuals can enter at any age, as success depends on skills, experience, and continuous learning. Many professionals transition into data science later in their careers by acquiring relevant knowledge in programming, statistics, and machine learning through online courses or certifications.

What are some common challenges faced by computational data scientists when working on cross-functional teams?

Computational data scientists often collaborate closely with professionals from diverse backgrounds, such as software engineers, domain experts, and business stakeholders. One common challenge is translating complex technical findings into actionable insights for non-technical team members. Additionally, aligning project goals and expectations across disciplines can require extra communication and flexibility. Overcoming these challenges often involves developing strong interpersonal skills, proactively clarifying requirements, and fostering a collaborative team culture.

Which is better, DS or CS?

Computational Data Science and Computer Science are related fields, but Data Science focuses on analyzing and interpreting data using statistical and machine learning techniques, while Computer Science emphasizes algorithms, programming, and software development. The choice depends on your career goals; Data Science roles often require skills in statistics, data visualization, and tools like Python or R, whereas Computer Science roles may focus more on software engineering and systems design.

What are the key skills and qualifications needed to thrive as a Computational Data Scientist, and why are they important?

To thrive as a Computational Data Scientist, you need a strong background in mathematics, statistics, programming (especially Python or R), and data analysis, often supported by a relevant degree in computer science, statistics, or a related field. Proficiency with data manipulation tools (like Pandas, NumPy), machine learning frameworks (such as TensorFlow or Scikit-learn), and cloud computing platforms is highly valued, along with experience using data visualization tools. Critical thinking, problem-solving, communication, and collaboration skills make someone stand out in this role. These abilities are crucial for extracting actionable insights from complex data, building effective models, and communicating findings to drive informed business decisions.

Is computational science a good career?

Computational Data Science is a growing field that combines programming, statistical analysis, and domain knowledge to solve complex problems using data. It offers opportunities in industries such as technology, finance, healthcare, and research, often requiring skills in machine learning, data visualization, and programming languages like Python or R. The career typically involves continuous learning and can provide competitive salaries and job stability.
Infographic showing various Computational Data Science job openings in Texas as of June 2026, with employment types broken down into 1% As Needed, 90% Full Time, and 9% Part Time. Highlights an 88% Physical, 3% Hybrid, and 9% Remote job distribution, with an average salary of $110,094 per year, or $52.9 per hour.
Computational Scientist

Full-time

Medical, Dental, Vision, Life, Retirement

Posted 8 days ago


ExxonMobil rating

6.1

Company rating: 6.1 out of 10

Based on 221 frontline employees who took The Breakroom Quiz

55th of 74 rated oil and gas companies


Job description

About us

At ExxonMobil, our vision is to lead in energy innovations that advance modern living and a net-zero future. As one of the world’s largest publicly traded energy and chemical companies, we are powered by a unique and diverse workforce fueled by the pride in what we do and what we stand for.

The success of our Upstream, Product Solutions and Low Carbon Solutions businesses is the result of the talent, curiosity and drive of our people. They bring solutions every day to optimize our strategy in energy, chemicals, lubricants and lower-emissions technologies. 

We invite you to bring your ideas to ExxonMobil to help create sustainable solutions that improve quality of life and meet society’s evolving needs. Learn more about our What and our Why and how we can work together.

About Houston

ExxonMobil's state-of-the-art campus north of Houston serves as home to its Upstream, Product Solutions and Low Carbon Solutions businesses and their associated service groups. The facility opened in 2014 and accommodates more than 10,000 employees and visitors. 
 

By bringing many global functional groups together, the campus provides employees with the tools and capabilities needed today, and in the future, to achieve business objectives and accelerate the discovery of new resources, technologies and products. It was designed to foster improved collaboration, creativity and innovation and enhance the company’s ability to attract, develop and retain the top talent in the industry. 
 

The campus is located in Spring, Texas, on 385 wooded acres immediately to the west of Interstate Highway 45 (I-45), at the intersection of I-45 and the Hardy Toll Road, approximately 25 miles from the cultural vibrancy of downtown Houston. 
 

The campus was constructed to the highest standards of energy efficiency and environmental stewardship. Its design incorporates extensive research into best practices in building and workplace design through extensive benchmarking of the world’s top academic, research, and corporate facilities. 
 

Learn more about what we do in Houston here.

What role you will play in our team

We are seeking a highly skilled and motivated Computational Scientist to join our team. This role involves developing and analyzing both physics-based and data-driven computational models to tackle a range of problems in the oil and gas industry.

What you will do
  • Work collaboratively across global, cross-disciplinary teams, and with third parties (academia, industry) to assess, accelerate pace of computational science technology development and deployment.
  • Frame computational challenge from business needs, develop solutions that strike a balance between accuracy and runtimes, develop solutions that merge physics and data incorporating uncertainty, develop novel approaches to constrain predictive models with field data.  
Skills & Qualifications
  • PhD from a recognized university in Engineering, Applied Mathematics, Geoscience, Computational Science, or a closely related field.
  • Experience in developing, applying, and analyzing physics-based models and developing related algorithms.
  • Strong background in multiscale and/or multiphysics mathematical modeling, scientific computing, and numerical analysis.
  • Hands-on experience with deep learning, including familiarity with a range of architectures (e.g., autoencoder, transformer, diffusion model, GAN) and their application to industrial, engineering, or scientific problems.
  • Experience in surrogate modeling approaches (e.g., deep learning, machine learning, physics-informed machine learning, reduced-order modeling, multi-fidelity methods, etc.) to reduce computational cost in decision-making processes (e.g., optimization, inverse problems, data assimilation) while maintaining fit-for-purpose accuracy.
  • Experience in formulating and solving convex and PDE constrained optimization problems.
  • Strong proficiency in programming/scripting languages like Python or C++/C#.
  • Proficiency in ML frameworks (PyTorch, TensorFlow, scikit-learn); experience with Databricks/Spark is a plus.
  • Experience with software engineering best practices including software testing, agile development, version control, and DevOps.
  • Experience working in Linux and High-Performance Computing environment is desirable but not required.
  • Prior experience in the upstream oil and gas industry is an advantage.
  • Strong communication skills and ability to work effectively in interdisciplinary teams to translate complex computational models into actionable insights.
Your benefits

An ExxonMobil career is one designed to last. Our commitment to you runs deep: our employees grow personally and professionally, with benefits built on our core categories of health, security, finance, and life.
 

We offer you: 
 

  • Pension Plan: Enrollment is automatic and at no cost to you. The basic benefit is a monthly annuity to be paid to you in retirement for the rest of your life. 
  • Savings Plan: You can contribute between 6% and 20% of your pay and are encouraged to enroll right away. If you contribute at least 6% to your savings plan, the Company will contribute a 7% match. 
  • Workplace Flexibility: We have several programs such as “Flex your Day”, providing ad-hoc flexibility around when and where you work, as well as longer-term programs such as leaves of absence and part-time work.
  • Comprehensive medical, dental, and vision plans. 
  • Culture of Health: Programs and resources to support your wellbeing. 
  • Employee Health Advisory Program: Provides confidential professional counseling for you and your family, including tools and resources promoting mental health and resiliency at no additional cost to you. 
  • Disability Plan: Income replacement for when you cannot work due to illness or injury occurring on or off the job. Enrollment is automatic and at no cost to you.
     

More information on our Company’s benefits can be found at  www.exxonmobilfamily.com.
 

Please note benefits may be changed from time to time without notice, subject to applicable law.

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Nothing herein is intended to override the corporate separateness of local entities. Working relationships discussed herein do not necessarily represent a reporting connection, but may reflect a functional guidance, stewardship, or service relationship. 

Exxon Mobil Corporation has numerous affiliates, many with names that include ExxonMobil, Exxon, Esso and Mobil. For convenience and simplicity, those terms and terms like corporation, company, our, we and its are sometimes used as abbreviated references to specific affiliates or affiliate groups. Abbreviated references describing global or regional operational organizations and global or regional business lines are also sometimes used for convenience and simplicity. Similarly, ExxonMobil has business relationships with thousands of customers, suppliers, governments, and others. For convenience and simplicity, words like venture, joint venture, partnership, co-venturer, and partner are used to indicate business relationships involving common activities and interests, and those words may not indicate precise legal relationships.


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