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Data Annotation Research Jobs in Prosper, TX (NOW HIRING)

HumanSignal

Delivery Lead

Dallas, TX · Remote

$110K - $140K/yr

Our customers ship better AI, faster, because we partner with their researchers from real-world data creation to annotation to delivery. We design and create datasets from scratch, recruit and manage ...

HumanSignal

High Volume (TOFU) Recruiter

Dallas, TX · On-site +1

$55K - $100K/yr

Our customers ship better AI, faster, because we partner with their researchers from real-world data creation to annotation to delivery. We design and create datasets from scratch, recruit and manage ...

Forhyre

Prompt Engineers

Plano, TX

Conduct extensive research to identify and curate relevant data sources for prompt development ... Solid knowledge of data collection, preprocessing, and annotation for prompt development.

Forhyre

Prompt Engineers

Dallas, TX

Conduct extensive research to identify and curate relevant data sources for prompt development ... Solid knowledge of data collection, preprocessing, and annotation for prompt development.

All JobsData Annotation Research JobsData Annotation Research Jobs in Prosper, TX

Data Annotation Research information

What qualifications do I need for data annotation?

Data annotation research roles typically require basic computer skills, attention to detail, and familiarity with annotation tools or platforms. A high school diploma or equivalent is usually sufficient, though some positions may prefer experience with data labeling, machine learning concepts, or specific software. Strong communication skills and the ability to work independently are also beneficial.

What are some common challenges faced in Data Annotation Research roles, and how can they be addressed?

Professionals in Data Annotation Research often encounter challenges such as maintaining consistency in labeling, dealing with ambiguous data, and managing large datasets efficiently. These issues can be addressed by following detailed annotation guidelines, participating in regular calibration sessions with the team, and utilizing annotation tools that support quality control checks. Collaboration with data scientists and project managers is essential to clarify ambiguities and ensure that annotated data meets the project's requirements. Staying proactive in communication and continuous learning helps to minimize errors and improve overall data quality.

Does data annotation actually pay?

Data annotation research jobs typically pay hourly or per task rates, with wages ranging from minimum wage to higher rates depending on experience and complexity of the work. Many positions are freelance or remote, requiring basic skills in data labeling tools and attention to detail. Payment is generally reliable, but rates vary by employer and project.

How hard is it to get hired by data annotation?

Getting hired for a data annotation research role typically requires basic computer skills, attention to detail, and sometimes familiarity with annotation tools or platforms. Many positions are entry-level and do not require advanced education, making the hiring process relatively accessible for those with the right skills and reliability.

What is the difference between Data Annotation Research vs Data Labeling Specialist?

AspectData Annotation ResearchData Labeling Specialist
CredentialsTypically requires a background in data science, research methods, or related fieldsOften requires basic technical skills and experience with labeling tools
Work EnvironmentResearch labs, tech companies, or remote research teamsData centers, tech companies, or remote labeling teams
Industry UsageUsed in AI/ML research, developing annotation methodologiesUsed in preparing datasets for machine learning models
Search & Comparison IntentUnderstanding research-focused roles in data annotationLooking for practical data labeling jobs

Data Annotation Research involves exploring new annotation techniques and improving data quality for AI models, often requiring research skills. In contrast, Data Labeling Specialists focus on applying existing labeling tools to annotate datasets efficiently. Both roles are essential in AI development but differ in scope and expertise.

Is data annotation real or fake?

Data annotation is a real and essential process in machine learning and AI development, involving labeling data such as images, text, or audio to train algorithms. Data annotation jobs require attention to detail and often use tools like labeling platforms or software, making them a legitimate employment opportunity in the tech industry.

What is data annotation research?

Data annotation research involves studying and developing methods for labeling data, such as images, text, or audio, to be used in training machine learning models. Researchers in this field focus on improving annotation accuracy, efficiency, and scalability, as well as addressing challenges like bias and consistency. This work is critical because high-quality annotated data is essential for building effective AI systems. Data annotation research often includes exploring new tools, techniques, and guidelines for human annotators or automated labeling systems.

What are the key skills and qualifications needed to thrive as a Data Annotation Researcher, and why are they important?

To thrive as a Data Annotation Researcher, you need strong attention to detail, analytical thinking, and familiarity with data labeling concepts, often supported by a degree in computer science, linguistics, or a related field. Experience with annotation platforms, data management tools, and sometimes knowledge of programming languages like Python are typically required. Excellent communication, problem-solving abilities, and the capacity to work independently set standout contributors apart. These skills ensure high-quality, accurate data labeling, which is crucial for developing reliable AI and machine learning models.
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Data Annotation Research jobs near you
NGS Data Scientist (Hybrid)

NGS Data Scientist (Hybrid)

Quest Diagnostics

Lewisville, TX • On-site

Full-time

Posted 11 days ago

ExamOne rating

6.6

Company rating: 6.6 out of 10

Based on 9 frontline employees who took The Breakroom Quiz

98th of 120 rated laboratories

Job description


We are seeking a highly motivated next generation sequencing (NGS) data analytic scientist to support Oncology Assay Development at Quest. As a member of the Oncology R&D team, this role will collaborate with other scientists and staff from cross-functional teams to develop, validate, and support cutting-edge genomic assays for oncology applications.
The ideal candidate will have a strong background in bioinformatics and/or genomic data analysis for somatic applications, translating complex data into actionable insights that drive product development, operational excellence and clinical impact.
This professional will work in a hybrid capacity requiring 3 days onsite at our Lewisville, TX facility.
Responsibilities
  • Perform bioinformatic analysis of genomics data from next generation sequencing. Specific data types include whole exome sequencing (WES), whole exome sequencing (WGS), and targeted DNA and RNA panel datasets generated from Illumina, Ion Torrent and PacBio sequencing technologies.
  • Curate, analyze, and interpret complex genomics datasets to support assay development, validation, and high throughput implementation
  • Present findings to drive decision-making in R&D product development, troubleshooting, and pipeline improvement
  • Design, develop, and deploy scalable and robust bioinformatics pipelines to support analytical validation data analysis for new oncology products
  • Partner with R&D, automation, laboratory operations, and software teams to understand and address the technical challenges and implement efficient solutions
  • Support production day-to-day operations, quickly pivoting focus to respond to incidents and requests
  • Monitor, execute, and improve sequence analysis workflows and automation
  • Produce charts and visualization for dashboards, reporting, and in support of data driven hypothesis testing
  • Track, troubleshoot and mitigate issues encountered in processing production samples to meet strict Turnaround Times (TAT)
  • Contribute to continuous improvement of analytical methods, workflows, and data quality standards

Qualifications
The ideal candidate will have a history of using bioinformatic technical skills to deliver accurate and timely results.
Required Work Experience:
  • Requires at least 4 (four) years of relevant industrial experience in data science and/or bioinformatics role
  • Master's degree, or a Ph.D. in bioinformatics, computational biology, molecular biology, chemistry, physics, statistics, or a related field
  • Experience with oncology genomics and somatic variant analysis

Preferred Work Experience:
  • Experience in NGS workflow development
  • Strong analytical skills with the ability to interpret and communicate experimental results
  • Ability to work collaboratively in a fast-paced, Cross-functional environment.
  • Experience with Software Development Life Cycle documentation procedure
  • Familiarity with assay validation, regulatory environment (CAP/CLIA), or clinical diagnostics

Skills:
  • Experience using scripting languages (Python, R, or similar) for scientific data analysis and visualization
  • Strong proficiency in NGS data processing, QC, and interpretation
  • Experience with analysis of next-generation sequencing data, preferably including Whole-Genome Sequencing, Variant calling, and Annotation
  • Excellent communication skills and the desire to work in a dynamic and collaborative environment

About the Team
Quest Diagnostics honors our service members and encourages veterans to apply.
While we appreciate and value our staffing partners, we do not accept unsolicited resumes from agencies. Quest will not be responsible for paying agency fees for any individual as to whom an agency has sent an unsolicited resume.
Equal Opportunity Employer: Race/Color/Sex/Sexual Orientation/Gender Identity/Religion/National Origin/Disability/Vets or any other legally protected status.

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