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Bioinformatics Data Analyst Jobs in Dallas, TX (NOW HIRING)

Vytwo

Bioinformatics Pipeline Test Engineer

Prosper, TX · On-site

Bioinformatics Pipeline Test Engineer Domain EXP : (Healthcare Domain) Experience: 2+ Years of exp ... data, AWS healthomics, Nextflo Type experience working on secondary analysis pipelines Essential ...

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All JobsBioinformatics Data Analyst JobsBioinformatics Data Analyst Jobs in Dallas, TX

Bioinformatics Data Analyst information

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How much do bioinformatics data analyst jobs pay per hour?

As of Sep 3, 2026, the average hourly pay for bioinformatics data analyst in Dallas, TX is $45.35, according to ZipRecruiter salary data. Most workers in this role earn between $35.67 and $48.51 per hour, depending on experience, location, and employer.

What is a bioinformatics data analyst?

Bioinformatics Data Analysts are professionals who use computational tools and methods to analyze biological data, such as genomic sequences or protein structures. They work at the intersection of biology, computer science, and statistics to interpret complex datasets and draw meaningful insights for research or clinical applications. Their work supports areas like drug discovery, personalized medicine, and evolutionary biology. Typically, they collaborate with biologists, software engineers, and statisticians to solve complex biological problems. Strong analytical skills and proficiency with data analysis software are essential for this role.

How do bioinformatics data analysts collaborate with researchers and other team members?

Bioinformatics Data Analysts often work closely with biologists, clinicians, and software engineers, acting as a bridge between experimental research and computational analysis. Collaboration usually involves interpreting experimental data, discussing analytical approaches, and presenting findings in a way that's accessible to non-technical stakeholders. Regular team meetings and project updates are common, and strong communication skills are essential for translating complex data insights into actionable information for the broader research team. This multidisciplinary teamwork fosters innovation and ensures that analyses align with the goals of larger research projects.

What are the key skills and qualifications needed to thrive as a bioinformatics data analyst?

To thrive as a Bioinformatics Data Analyst, a solid background in biology, statistics, and programming (often with a degree in bioinformatics, computational biology, or a related field) is essential. Proficiency with tools such as R, Python, SQL, and bioinformatics software like BLAST or Bioconductor, as well as experience with large datasets and relevant certifications, is typically required. Strong analytical thinking, attention to detail, and effective communication skills help analysts interpret complex data and collaborate with interdisciplinary teams. These skills ensure accurate data analysis, meaningful biological insights, and successful project outcomes in research or clinical settings.

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

AspectBioinformatics Data AnalystBioinformatics Scientist
Required CredentialsBachelor's or Master's in Bioinformatics, Biology, or related fieldsMaster's or PhD in Bioinformatics, Computational Biology, or related fields
Work EnvironmentData analysis teams, research labs, healthcare settingsResearch projects, development of algorithms, scientific publications
Employer & Industry UsageBiotech companies, healthcare institutions, research organizationsAcademic institutions, biotech firms, pharmaceutical companies
Common Search & ComparisonOften compared for data analysis roles in bioinformaticsMore research-focused, involved in algorithm development

Bioinformatics Data Analysts primarily focus on analyzing biological data using existing tools, while Bioinformatics Scientists develop new algorithms and conduct research. Both roles require strong computational skills, but the Scientist role typically involves more advanced research and innovation.

What are popular job titles related to Bioinformatics Data Analyst jobs in Dallas, TX?

For Bioinformatics Data Analyst jobs in Dallas, TX, the most frequently searched job titles are:

What job categories do people searching Bioinformatics Data Analyst jobs in Dallas, TX look for?

The top searched job categories for Bioinformatics Data Analyst jobs in Dallas, TX are:

What cities near Dallas, TX are hiring for Bioinformatics Data Analyst jobs?

Cities near Dallas, TX with the most Bioinformatics Data Analyst job openings:

Bioinformatics Data Analyst jobs near you
Infographic showing various Bioinformatics Data Analyst job openings in Dallas, TX as of August 2026, with employment types broken down into 72% Full Time, 6% Part Time, 4% Temporary, and 18% Contract. Highlights an 78% In-person, and 22% Remote job distribution, with an average salary of $94,322 per year, or $45.3 per hour.

ML / Bioinformatics Data Scientist

IT America Inc

Dallas, TX • Remote

Contractor

Re-posted 4 days ago

Job description

Position: ML / Bioinformatics Data Scientist

Location: Remote (PST work hours)

Duration: Long term contract

About the Role:

We are seeking a highly motivated and collaborative Bioinformatics/ML scientist to join the Computational biology & Medicine department in Computational Sciences COE (Center of Excellence) within Genentech’s Research and Early Development (gRED). The successful candidate will contribute to a cross-functional project that will apply Machine Learning (ML) models to multi-modal datasets collected from clinical trials. This role requires a deep understanding of application of Machine Learning models, a background in biology, a passion for innovation, and a commitment to improving healthcare outcomes through cutting-edge technology.

We are looking for exceptional researchers with a passion for interdisciplinary research and technical problem-solving, and a proven ability to develop and implement research ideas. The candidate is expected to have worked on previous ML modeling projects and applying them to multi-modal datasets to be considered.

About the Project:

The goal of this project is to develop a machine learning model to predict a patient's risk for drug-induced liver toxicity based on a wide variety of patient characteristics including clinical, genetics, omics and safety labs. The focus will be harmonizing these diverse data sources, deriving new features, and  building machine learning models designed to identify a predictive signature that can distinguish between at-risk and not-at-risk patient populations.

Key Responsibilities:

  • Data centralization and harmonization
  • Applying ML methods on assembled dataset to identify patients’ risk for drug-induced liver toxicity.
  • Collaborate with interdisciplinary and cross-functional teams including biologists, chemists, data scientists, and other stakeholders.

Educational Background:

  • PhD degree in quantitative field ( e.g., Computer Science, Computational Biology, Bioinformatics, Statistics, Mathematics) 

Experience:

  • Proven track record of working with statistical modeling techniques, including ML methods, is required
  • Demonstrated interest in problems across biology as applied to the discovery and development of treatments for disease is preferred

Technical Skills:

  • Data Science & Programming: Expertise in Python/R for data manipulation, statistical analysis, and ML model building (required)
  • Multimodal Data & Modeling: Proven ability to work with diverse data types (omics, clinical, imaging) (required).
  • Knowledge of statistics and experience with survival analysis (required)
  • Domain & AI-specific Skills: Experience with NLP/LLMs for feature extraction from unstructured text, and a strong background in a neuroscience (preferred)

Soft Skills:

  • Excellent communication, collaboration, and problem-solving skills (required).

Publications:

  • Strong publication record and experience contributing to research communities.