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Computational Data Analytics Jobs in Utah (NOW HIRING)

micro1 AI

Cheminformatics Specialist - Remote

Provo, UT · Remote

$80 - $110/hr

Analyze and interpret small-molecule and drug discovery datasets using advanced computational ... clinical data sources. * Provide expert insights on structure-activity and structure-property ...

Utah State University

Postdoctoral Fellow I

Logan, UT

$42K - $57K/yr

Ph.D. degree in Computational Biology, Genomics, Bioinformatics, Computer Science or highly related field. * Experience in developing Machine Learning-based models and large-scale data analysis ...

Utah State University

Postdoctoral Fellow I

Logan, UT · On-site

$42K - $57K/yr

Ph.D. degree in Computational Biology, Genomics, Bioinformatics, Computer Science or highly related field. * Experience in developing Machine Learning-based models and large-scale data analysis ...

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Computational Data Analytics information

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How much do computational data analytics jobs pay per hour?

As of Aug 19, 2026, the average hourly pay for computational data analytics in Utah is $49.84, according to ZipRecruiter salary data. Most workers in this role earn between $40.05 and $56.44 per hour, depending on experience, location, and employer.

What is computational data analytics?

Computational data analytics is the process of using computational methods, algorithms, and systems to analyze large and complex datasets. This field combines principles from computer science, mathematics, and statistics to extract meaningful insights and patterns from data. Professionals in computational data analytics use tools such as machine learning, data mining, and statistical modeling to solve real-world problems in various industries. Their work often involves programming, data visualization, and working with big data platforms.

How does a computational data analyst typically collaborate with cross-functional teams to deliver data-driven insights?

Computational Data Analysts frequently work alongside professionals from various departments, such as engineering, product management, and business strategy. They gather requirements, clarify analysis goals, and present findings in clear, actionable terms. Regular meetings and collaborative tools are often used to ensure alignment, while analysts translate complex data patterns into practical recommendations that support decision-making across the organization. This teamwork not only enhances the impact of their analyses but also provides valuable opportunities for learning and professional growth.

What are the key skills and qualifications needed to thrive as a computational data analytics professional, and why are they important?

To thrive as a Computational Data Analytics professional, you need strong quantitative skills, proficiency in statistics, and expertise in data manipulation, typically supported by a degree in computer science, mathematics, or a related field. Familiarity with programming languages like Python or R, experience with data visualization tools (e.g., Tableau, Power BI), and knowledge of machine learning frameworks are commonly required. Excellent problem-solving abilities, effective communication, and the capacity to work collaboratively make candidates stand out. These skills enable professionals to extract actionable insights from complex datasets, drive informed decision-making, and add significant value to organizations.

What is the difference between Computational Data Analytics vs Data Scientist?

AspectComputational Data AnalyticsData Scientist
Required CredentialsBachelor's or Master's in Data Science, Computer Science, or related fieldsBachelor's or Master's in Data Science, Computer Science, Statistics, or related fields
Work EnvironmentData analysis teams, research labs, tech companiesData analysis teams, research labs, tech companies
Employer & Industry UsageTech, finance, healthcare, academiaTech, finance, healthcare, academia
Common Search & ComparisonYesYes

Computational Data Analytics focuses on developing algorithms and computational methods to analyze large datasets, often emphasizing programming and algorithm design. Data Scientists combine statistical analysis, machine learning, and domain expertise to interpret data and generate insights. While both roles require similar educational backgrounds and work environments, Computational Data Analytics leans more toward algorithm development, whereas Data Scientists focus on modeling and interpretation.

What are popular job titles related to Computational Data Analytics jobs in Utah?

For Computational Data Analytics jobs in Utah, the most frequently searched job titles are:

What job categories do people searching Computational Data Analytics jobs in Utah look for?

The top searched job categories for Computational Data Analytics jobs in Utah are:

Computational Data Analytics jobs near you

AI Training Specialist - Cheminformatics

micro1 AI

Saint George, UT • Remote

$80 - $110/hr

Part-time

Posted 17 days ago

Job description

Role Title: Computational Biology & Cheminformatics Expert


Role Type: Contractor


Location: Remote


micro1 is engaging Computational Biology & Cheminformatics Experts to contribute their expertise to a customer’s computational drug discovery project. In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required — your domain knowledge is what matters.


Scope of Work

  1. Analyze and interpret small-molecule and drug discovery datasets using advanced computational biology, bioinformatics, and cheminformatics methods.
  2. Curate, annotate, and validate chemical and biological datasets (e.g., ChEMBL, PubChem, DrugBank) to support AI-driven discovery platforms.
  3. Evaluate compound-target interactions, ADMET properties, and lead optimization strategies by integrating chemical, biological, and clinical data sources.
  4. Provide expert insights on structure-activity and structure-property relationships (SAR/SPR), medicinal chemistry approaches, and experimental design considerations.
  5. Build and implement code-based benchmark tasks (e.g., terminal/CLI-based environments) that reflect realistic computational drug discovery scenarios.
  6. Develop reproducible environments (e.g., using Docker) and automated testing pipelines to ensure task correctness and solvability.
  7. Assess and review AI-generated outputs for scientific rigor, accuracy, and practical relevance, delivering detailed written feedback and recommendations.


Preferred Qualifications

  1. Advanced expertise in Computational Biology, Cheminformatics, Medicinal Chemistry, Biochemistry, or related fields; advanced degree (PhD, MSc, PharmD) highly valued but not strictly required.
  2. Strong coding proficiency in Python (beyond analysis scripts), with hands-on experience building tools, pipelines, or testable code; familiarity with Git, GitHub, and Docker.
  3. Extensive experience with cheminformatics toolkits and platforms such as RDKit, KNIME, Schrödinger, OpenEye, or MOE.
  4. Proven track record in small-molecule drug discovery, SAR/QSAR evaluation, ADMET prediction, or virtual screening workflows.
  5. Comfort working with public chemical and bioactivity databases and integrating diverse datasets for scientific analysis.
  6. Demonstrated ability to clearly communicate complex chemical and biological concepts in written feedback and reports.
  7. Experience participating in multidisciplinary and/or remote projects; familiarity with AI-assisted coding tools is a plus.

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