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Data Analyst Github Jobs in Utah (NOW HIRING)

micro1 AI

Bioinformatics Software Engineer

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 ...

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 ...

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Data Analyst Github information

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$31K

$75.2K

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How much do data analyst github jobs pay per year?

As of Aug 16, 2026, the average yearly pay for data analyst github in Utah is $75,233.00, according to ZipRecruiter salary data. Most workers in this role earn between $56,900.00 and $88,300.00 per year, depending on experience, location, and employer.

How does a data analyst at GitHub typically collaborate with engineering and product teams?

At GitHub, Data Analysts frequently work alongside engineering and product teams to translate business questions into actionable data insights. They participate in cross-functional meetings, help define key metrics, and build dashboards or reports tailored to the needs of different stakeholders. Effective collaboration requires strong communication skills, as analysts must explain complex data findings to both technical and non-technical colleagues. This collaborative environment fosters continual learning and often provides opportunities to contribute to strategic decisions that impact the direction of products and features.

What are the key skills and qualifications needed to thrive as a data analyst at GitHub, and why are they important?

To thrive as a Data Analyst on GitHub, you need strong analytical skills, experience in statistics, and proficiency in data manipulation using languages like Python or SQL, often backed by a relevant degree. Familiarity with data visualization tools (e.g., Tableau, Power BI), Git version control, and GitHub workflows is essential, and certifications in data analysis or related fields are advantageous. Attention to detail, problem-solving, and effective communication are vital soft skills for collaborating on open-source projects and sharing insights. These competencies enable accurate data-driven decision-making, efficient project collaboration, and impactful contributions to the GitHub community.

What is a data analyst at GitHub?

Data Analysts on GitHub are professionals or contributors who use the platform to share, collaborate, and manage data analysis projects. They leverage GitHub to store datasets, share scripts and code (often in languages like Python or R), and document their analyses using tools like Jupyter Notebooks or Markdown. GitHub enables Data Analysts to version-control their work, collaborate with others through pull requests and issues, and showcase their portfolios to potential employers or collaborators.

Is GitHub good for data analysts?

GitHub is a valuable tool for data analysts as it facilitates version control, collaboration, and sharing of data projects and code. Many data analysts use GitHub to showcase their work, collaborate with teams, and manage project documentation, often integrating it with tools like Jupyter notebooks and data visualization libraries.

What is the difference between Data Analyst Github vs Data Scientist?

AspectData Analyst GithubData Scientist
Required CredentialsBachelor's in Data Analytics, Statistics, or related field; proficiency in SQL, Excel, and visualization toolsBachelor's or Master's in Data Science, Computer Science, or related; knowledge of programming languages like Python or R, machine learning
Work EnvironmentCollaborates with teams to analyze data, create dashboards, and support decision-makingBuilds models, develops algorithms, and performs advanced statistical analysis
Employer & Industry UsageUsed across industries for reporting, data visualization, and business insightsApplied in AI, predictive modeling, and complex data analysis projects

While both roles involve working with data, Data Analyst Github focuses on data visualization, reporting, and supporting business decisions, often using tools like SQL and Excel. Data Scientists perform advanced analytics, build predictive models, and require programming skills in Python or R. The roles overlap in data handling but differ in complexity and technical depth.

What are popular job titles related to Data Analyst Github jobs in Utah?

For Data Analyst Github jobs in Utah, the most frequently searched job titles are:

What job categories do people searching Data Analyst Github jobs in Utah look for?

The top searched job categories for Data Analyst Github jobs in Utah are:

What cities in Utah are hiring for Data Analyst Github jobs?

Cities in Utah with the most Data Analyst Github job openings:

Data Analyst Github jobs near you
Infographic showing various Data Analyst Github job openings in Utah as of August 2026, with employment types broken down into 1% As Needed, 82% Full Time, 13% Part Time, and 4% Contract. Highlights an 88% Physical, 3% Hybrid, and 9% Remote job distribution, with an average salary of $75,233 per year, or $36.2 per hour.

Bioinformatics Research Scientist - AI Reviewer

micro1 AI

Salt Lake City, UT • Remote

$80 - $110/hr

Part-time

Posted 13 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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