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Data Interpretation Jobs in Texas (NOW HIRING)

micro1 AI

AI Training Specialist - Life Sciences

Waco, TX · Remote

$90 - $120/hr

Offer subject matter expertise on experimental design and data interpretation within medicinal chemistry. * Assess AI-generated outputs for scientific accuracy, relevance, and reliability. * Deliver ...

micro1 AI

AI Training Specialist - Life Sciences

Allen, TX · Remote

$90 - $120/hr

Offer subject matter expertise on experimental design and data interpretation within medicinal chemistry. * Assess AI-generated outputs for scientific accuracy, relevance, and reliability. * Deliver ...

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All JobsData Interpretation JobsData Interpretation Jobs in Texas

Data Interpretation information

See Texas salary details

$42.9K

$153.7K

$226.9K

How much do data interpretation jobs pay per year?

As of Aug 16, 2026, the average yearly pay for data interpretation in Texas is $153,740.00, according to ZipRecruiter salary data. Most workers in this role earn between $124,400.00 and $158,400.00 per year, depending on experience, location, and employer.

What is a data interpretation job?

A data interpretation job involves analyzing data sets to extract meaningful insights, often using tools like Excel, SQL, or data visualization software. Professionals in this role interpret statistical results, identify trends, and support decision-making processes within organizations.

What is the difference between Data Interpretation vs Data Analysis?

AspectData InterpretationData Analysis
Primary FocusUnderstanding and explaining data insightsExamining data to identify patterns and trends
Skills RequiredAnalytical thinking, communication, visualizationStatistical skills, programming, data manipulation
Tools UsedCharts, reports, visualization softwareExcel, SQL, Python, R
Work EnvironmentBusiness meetings, reporting, presentationsData warehouses, analysis platforms

Data Interpretation involves explaining what data means and communicating insights, often to non-technical stakeholders. Data Analysis focuses on examining raw data to uncover patterns and trends, often using statistical tools. While both roles require analytical skills, Data Interpretation emphasizes understanding and storytelling, whereas Data Analysis emphasizes technical data manipulation and discovery.

What is data interpretation?

Data interpretation is the process of analyzing data sets and extracting meaningful insights or conclusions from them. It involves examining data in various forms, such as tables, charts, and graphs, to identify patterns, trends, and relationships. This skill is essential in many fields, including business, research, and education, as it helps inform decision-making and problem-solving. Effective data interpretation requires critical thinking, attention to detail, and a good understanding of the context in which the data was collected.

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

To thrive as a Data Interpreter, you need strong analytical skills, statistical knowledge, and a solid understanding of data visualization, often supported by a degree in mathematics, statistics, or a related field. Familiarity with tools like Microsoft Excel, Tableau, or Python for data analysis and visualization is typically required. Attention to detail, critical thinking, and the ability to communicate complex findings clearly are valuable soft skills in this role. These skills ensure accurate data-driven insights and effective communication to guide business decisions.

How does a data interpretation specialist typically collaborate with cross-functional teams to drive business insights?

Data Interpretation specialists frequently work alongside analysts, business managers, and IT teams to translate raw data into actionable insights. They are responsible for clarifying data trends, ensuring data accuracy, and contextualizing findings for non-technical stakeholders, often presenting results through visualizations or reports. Effective collaboration requires strong communication skills and a good understanding of both business objectives and technical data limitations. This teamwork enables organizations to make informed decisions and continuously improve processes based on data-driven evidence.

What are popular job titles related to Data Interpretation jobs in Texas?

For Data Interpretation jobs in Texas, the most frequently searched job titles are:

What job categories do people searching Data Interpretation jobs in Texas look for?

The top searched job categories for Data Interpretation jobs in Texas are:

Data Interpretation jobs near you
Infographic showing various Data Interpretation job openings in Texas as of August 2026, with employment types broken down into 1% As Needed, 86% Full Time, 10% Part Time, and 3% Contract. Highlights an 86% Physical, 4% Hybrid, and 10% Remote job distribution, with an average salary of $153,740 per year, or $73.9 per hour.

Digital Chemistry Specialist - Remote

micro1 AI

Sugar Land, TX • Remote

$90 - $120/hr

Part-time

Posted 19 days ago

Job description

Role Title: Bioinformatics Scientist


Role Type: Contractor


Location: Remote


micro1 is engaging Bioinformatics Scientists to contribute their specialized expertise to a customer's innovative 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 complex datasets related to medicinal chemistry using advanced bioinformatics methodologies.
  2. Provide detailed scientific input and content to support the development and training of AI models.
  3. Curate, annotate, and validate datasets relevant to drug discovery and molecular analysis.
  4. Evaluate and synthesize findings from biological, chemical, and clinical data sources.
  5. Offer subject matter expertise on experimental design and data interpretation within medicinal chemistry.
  6. Assess AI-generated outputs for scientific accuracy, relevance, and reliability.
  7. Deliver comprehensive written feedback and actionable recommendations for model improvement.


Preferred Qualifications

  1. Advanced degree (e.g., PhD or MSc) in Bioinformatics, Computational Biology, Medicinal Chemistry, or a related discipline.
  2. In-depth knowledge of medicinal chemistry concepts, including structure-activity relationships and drug design principles.
  3. Demonstrated experience in handling and interpreting large-scale omics or cheminformatics datasets.
  4. Familiarity with software tools, databases, and programming languages commonly used in bioinformatics (e.g., Python, R, RDKit, KNIME).
  5. Strong scientific communication skills, with the ability to clearly articulate complex ideas and technical concepts.
  6. Proven track record of contributing to research projects at the intersection of biology, chemistry, and data science.
  7. Experience collaborating in multidisciplinary or remote project environments is advantageous.

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