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Computational Data Analytics Jobs (NOW HIRING)

Trans Ova Genetics

Computational Biologist

Madison, WI · On-site

URUS is seeking a Computational Biologist to join our Innovation group as part of the team focused ... Data Analysis & Machine Learning: Strong skills in data mining, machine learning (deep learning ...

Urus Group LP

Computational Biologist

Madison, WI

URUS is seeking a Computational Biologist to join our Innovation group as part of the team focused ... Data Analysis & Machine Learning: Strong skills in data mining, machine learning (deep learning ...

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Computational Data Analytics information

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How much do computational data analytics jobs pay per hour?

As of Jun 25, 2026, the average hourly pay for computational data analytics in the United States is $54.75, according to ZipRecruiter salary data. Most workers in this role earn between $43.99 and $62.02 per hour, depending on experience, location, and employer.

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.

Is 40 too late for data science?

Computational Data Analytics professionals can enter the field at any age, as success depends on skills, experience, and continuous learning. Many data scientists start or transition into the field later in life by acquiring relevant certifications, programming skills, and domain knowledge. Age is less important than your ability to adapt and develop expertise in tools like Python, R, and SQL.

Will AI replace data analysts?

AI tools can automate routine data processing and analysis tasks, but data analysts are essential for interpreting complex insights, making strategic decisions, and applying domain knowledge. The role of a data analyst involves skills like critical thinking, communication, and understanding business context, which are difficult for AI to fully replicate. Therefore, AI is more likely to augment rather than replace data analysts in the foreseeable future.

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 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.

Is data analytics a high paying job?

Data analytics is generally considered a well-paying field, especially for roles like computational data analysts who possess strong skills in programming, statistics, and data visualization tools. Salaries tend to increase with experience, certifications, and advanced technical expertise, making it a lucrative career option in the tech industry.

What field is the highest paid data analyst?

Data analysts working in finance, investment banking, and technology tend to have the highest salaries, especially those with advanced skills in machine learning, statistical analysis, and proficiency in tools like SQL and Python. Specializing in these high-demand industries and obtaining relevant certifications can lead to higher compensation.
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Computational Data Analytics jobs near you
Infographic showing various Computational Data Analytics job openings in the United States as of June 2026, with employment types broken down into 17% Full Time, 10% Part Time, 66% Contract, and 7% Nights. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $113,873 per year, or $54.7 per hour.
Computational Biologist

Computational Biologist

University of Massachusetts Medical School

Worcester, MA • On-site

$80K - $90K/yr

Full-time

Posted 11 days ago

Job description

The Computational Biologist be part of an interdisciplinary research group combining systems biology, immunology, and human genetics to uncover the mechanisms that drive autoimmune disease. The lab leads large-scale efforts such as the VIGOR family-based vitiligo cohort (bigor.umassmed.edu) and multi-omic studies of lupus and cutaneous autoimmunity, integrating data across molecular, cellular, and clinical scales.

This position will bridge two complementary areas of research:

  1. Molecular systems immunology, involving the analysis of single-cell and spatial transcriptomic, epigenomic, and proteomic datasets to dissect cell states and communication networks in diseased and healthy tissues.
  2. Genetic and longitudinal modeling, integrating genomic variation with real-world longitudinal data—including proteomics, wearable device metrics, survey responses, and clinical measures—to build predictive and causal models of disease initiation and progression.

The ideal candidate combines strong computational and statistical skills with a biological curiosity about how genetic and environmental factors jointly shape immune dysregulation.


Responsibilities

  • Process, analyze, and interpret large-scale datasets including bulk and single-cell RNA-seq, ATAC-seq, proteomics, and spatial transcriptomics.
  • Develop new analysis methods as needed and as they arise during investigations
  • Perform clustering, trajectory inference, and regulatory network reconstruction to define immune cell states and pathways relevant to autoimmune pathogenesis.
  • Work closely with clinicians, immunologists, and experimentalists to formulate biologically grounded hypotheses and computational analyses.
  • Integrate genetic, molecular, and clinical features to identify mediators linking genotype to phenotype using mediation and causal inference frameworks (e.g., Bayesian networks).
  • Combine data from wearable sensors (e.g., Fitbit activity, sleep, heart rate), clinical surveys, and biomarker measurements to model temporal dynamics of disease activity.
  • Present findings in lab meetings, consortium calls, and scientific conferences; contribute to manuscripts and grant proposals.
  • Generate publication-quality figures and interactive visualizations that communicate complex data intuitively.

Required Qualifications

  • Master’s degree in Computational Biology, Bioinformatics, Genetics, Statistics, Physics, Math or a related quantitative field; Ph.D. strongly preferred.
  • 1-3 years of related experience
  • Strong proficiency in R or Python, statistical modeling, and data visualization.
  • Strong understanding of linear models, mixed-effect models, and in general machine learning approaches to complex datasets.
  • Experience working in Unix/Linux environments and using HPC or cloud-based computational resources.

Preferred Qualifications

  • Background in human genetics or clinical genomics, including genotype imputation, association testing, and fine-mapping.
  • Experience with integrative or multi-omic data analysis and familiarity with single-cell and spatial transcriptomic data.
  • Knowledge of causal inference, longitudinal modeling, or Bayesian hierarchical modeling.
  • Exposure to wearable-device or digital-phenotyping datasets and experience linking such data to molecular or clinical outcomes.
  • Understanding of immunology or autoimmune disease biology.
  • Familiarity with containerization (Docker/Singularity), workflow management systems (Snakemake, Nextflow), and reproducible-research practices.

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