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Computational Data Science Jobs in Texas (NOW HIRING)

As a Data Scientist/Data Science Specialist for Adidev Technologies Inc., you will be enhancing and ... S. in Computer Science, Computational Physics, Operations Research, Geospatial Sciences, Remote ...

As a Data Scientist/Data Science Specialist for Adidev Technologies Inc., you will be enhancing and ... S. in Computer Science, Computational Physics, Operations Research, Geospatial Sciences, Remote ...

Seeking Data Scientist that performs tasks associated with Big Data Platform management, utilizes ... other science disciplines with a substantial computational component may be considered if it ...

Seeking Data Scientist that performs tasks associated with Big Data Platform management, utilizes ... other science disciplines with a substantial computational component may be considered if it ...

Seeking Data Scientist that performs tasks associated with Big Data Platform management, utilizes ... other science disciplines with a substantial computational component may be considered if it ...

... data science approach by developing novel and/or adapting existing computational methods • Strong skills in communicating and presenting data-derived insights to non-technical audiences ...

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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 Jul 13, 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, programming knowledge, and familiarity with data management environments.

Is computational science in demand?

Computational Data Science is in high demand across industries such as technology, finance, healthcare, and research, driven by the increasing reliance on data analysis, machine learning, and big data tools. Professionals with skills in programming, statistical analysis, and data modeling are sought after for roles involving data-driven decision making and automation.

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. Age is generally not a barrier if you develop the necessary technical skills and stay current with industry tools.

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.

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.

What is the highest paid job in data science?

The highest paid roles in data science are often senior positions such as Lead Data Scientist, Machine Learning Director, or Chief Data Officer, with salaries exceeding $150,000 annually. These roles typically require advanced skills in machine learning, big data tools, and leadership experience. Compensation varies by industry, location, and company size.
Infographic showing various Computational Data Science job openings in Texas as of July 2026, with employment types broken down into 1% Locum Tenens, 2% Internship, 74% Full Time, 21% Part Time, 1% Nights, and 1% Summer. Highlights an 70% Physical, 1% Hybrid, and 29% Remote job distribution, with an average salary of $110,094 per year, or $52.9 per hour.
Senior Data Scientist Immunotherapy Platform

Senior Data Scientist Immunotherapy Platform

MD Anderson

Houston, TX • On-site

Other

Medical, Dental, Vision, Life, Retirement, PTO

Posted 5 days ago


MD Anderson Cancer Center rating

8.4

Company rating: 8.4 out of 10

Based on 169 frontline employees who took The Breakroom Quiz

27th of 882 rated healthcare providers


Job description

UT MD Anderson is a leading institution focused on cancer care, research, education, and prevention. UT MD Anderson is dedicated to advancing scientific discovery and translating breakthrough research into impactful therapies for patients worldwide. The Senior Data Scientist (Data Infrastructure & Engineering) plays a critical role within the Immunotherapy Platform (IMT), supporting multi-modal cancer research through the development of scalable and robust data systems.

The Senior Data Scientist (Data Infrastructure & Engineering) will lead efforts to integrate diverse biomedical datasets into a unified ecosystem, while the Senior Data Scientist (Data Infrastructure & Engineering) also collaborates closely with researchers and computational teams to enable efficient, AI-ready workflows. Ideal candidate has experience developing AI-ready models and scalable computational data pipelines, with strong ability to collaborate across multidisciplinary teams to support complex research and data infrastructure needs. Experience working in a academic or healthcare environment.

Why Us. This role offers the opportunity to build foundational data systems that directly enable cutting-edge cancer immunotherapy research at UT MD Anderson. By contributing to scalable, AI-ready data infrastructure, individuals in this role will have a meaningful impact on accelerating scientific discovery while developing advanced technical expertise in a collaborative and innovation-driven environment that supports professional growth and work-life balance.

Employer-paid medical coverage starting day one for employees working 30+ hours/week, plus optional group dental, vision, life, AD&D, and disability insurance. Accruals for PTO and Extended Illness Bank, plus paid holidays, wellness, childcare, and other leave options. Tuition Assistance Program after six months of service and access to extensive wellness, fitness, and employee resource groups.

Defined-benefit pension through the Teachers Retirement System, voluntary retirement plans, and employer-paid life and reduced salary protection programs. Responsibilities Data Infrastructure & Engineering Design and implement biomedical data systems for multi-modal datasets including genomic, single-cell, spatial, proteomic, and clinical data Develop and maintain an internal data registry and metadata tracking systems Design databases and data models using SQL/NoSQL technologies, schema design, and APIs Harmonize and standardize data across datasets and platforms Pipeline Development & Systems Develop pipelines for data ingestion, transformation, and integration using ETL/ELT processes Utilize workflow orchestration tools such as Nextflow and Snakemake Apply strong programming skills in Python, with R as a plus Leverage HPC and/or cloud-based environments for scalable processing Implement version control and reproducible workflows using Git and container technologies Enable lightweight data access and integration with visualization tools or internal data portals Project Development Design and implement a centralized internal data system for IMT Develop and enforce standardized data schemas across modalities Enable efficient querying, access, and reuse of datasets Collaborate with computational scientists to support analysis and modeling workflows Optimize data pipelines to improve scalability and reduce turnaround time Establish best practices for data governance, documentation, and reproducibility Support development of internal data access interfaces such as portals or dashboards Research Support Support integration of multi-modal datasets for downstream analysis Enable data structures compatible with AI and machine learning workflows Collaborate with research teams to translate analytical needs into scalable systems Evaluate and adopt emerging tools and standards in biomedical data infrastructure Contribute to publications through development of computational workflows and systems Other duties as assigned. EDUCATION: Required: Bachelor's Degree Biomedical Engineering, Electrical Engineering, Computer Engineering, Physics, Applied Mathematics, Science, Engineering, Computer Science, Statistics, Computational Biology, or related field.

Master's Degree Science, Engineering or related field. PhD Science, Engineering or related field. EXPERIENCE: Required: Five years experience in scientific software or industry programming with a concentration in scientific computing.

With Master's degree, three years experience. With PhD, one year experience. Preferred: Experience working with biomedical or genomics data in healthcare or academic environment.

Experience with AI models. Experience computational pipelines. Experience supporting research or analytical teams.

The University of Texas MD Anderson Cancer Center offers excellent benefits, including medical, dental, paid time off, retirement, tuition benefits, educational opportunities, and individual and team recognition. This position may be responsible for maintaining the security and integrity of critical infrastructure, as defined in Section 113.001(2) of the Texas Business and Commerce Code and therefore may require routine reviews and screening. The ability to satisfy and maintain all requirements necessary to ensure the continued security and integrity of such infrastructure is a condition of hire and continued employment

It is the policy of The University of Texas MD Anderson Cancer Center to provide equal employment opportunity without regard to race, color, religion, age, national origin, sex, gender, sexual orientation, gender identity/expression, disability, protected veteran status, genetic information, or any other basis protected by institutional policy or by federal, state, or local laws unless such distinction is required by law.http://www.mdanderson.org/about-us/legal-and-policy/legal-statements/eeo-affirmative-action.html Additional Information Requisition ID: 180479 Employment Status: Full-Time Employee Status: Regular Work Week: Days Minimum Salary: US Dollar (USD) 123,000 Midpoint Salary: US Dollar (USD) 154,000 Maximum Salary : US Dollar (USD) 185,000 FLSA: exempt and not eligible for overtime pay Fund Type: Soft Work Location: Hybrid Onsite/Remote Pivotal Position: Yes Referral Bonus Available?: Yes Relocation Assistance Available?: Yes #LI-Hybrid Apply


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