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$57

How much do data labeling jobs pay per hour?

As of Aug 22, 2026, the average hourly pay for data labeling in Maryland is $24.43, according to ZipRecruiter salary data. Most workers in this role earn between $16.05 and $28.03 per hour, depending on experience, location, and employer.

What is data labeling?

A Data Labeling job involves annotating or tagging data, such as images, text, audio, or videos, to help train machine learning models. Labelers follow specific guidelines to classify data accurately so that AI systems can learn patterns and make predictions. This role is essential in fields like computer vision, natural language processing, and speech recognition. Strong attention to detail and consistency are crucial for ensuring high-quality training datasets.

What are the typical day-to-day responsibilities of a data labeling professional?

A Data Labeling professional is primarily responsible for reviewing and accurately tagging images, text, audio, or video data according to specified guidelines. Daily tasks often include managing large datasets, using annotation software to classify data, and verifying the quality and accuracy of the labels. Collaboration with data scientists, project managers, and other annotators is common, especially when clarifying labeling guidelines or resolving ambiguities. Attention to detail is crucial, as high-quality labeled data directly impacts the effectiveness of machine learning models and AI applications. Most positions are structured in team environments, where productivity and communication skills help ensure project deadlines are met.

What are the key skills and qualifications needed to thrive in data labeling, and why are they important?

To thrive in Data Labeling, you need meticulous attention to detail, strong analytical abilities, and basic computer literacy, often supported by a high school diploma or equivalent. Familiarity with data annotation tools, image or text editing software, and experience with platforms like Labelbox or Amazon SageMaker Ground Truth are commonly advantageous. Exceptional concentration, patience, and the ability to follow precise instructions are valuable soft skills in this position. These skills and qualities are essential for ensuring the accuracy and consistency of labeled datasets, which are critical for training reliable AI and machine learning models.

How can I get started in data labeling?

To get started in data labeling, you should develop basic skills in data annotation tools and understand labeling guidelines for different data types such as images, text, or audio. Many entry-level positions require attention to detail and sometimes a background in relevant fields like computer science or linguistics; online courses and practice datasets can help build your skills. Additionally, creating a strong profile on job platforms and applying to companies that offer remote or flexible data labeling roles can increase your chances of starting in this field.

How much do data labelers make?

Data labelers typically earn between $10 and $20 per hour, depending on experience, complexity of tasks, and the platform or employer. Some may work as freelancers or part-time, with pay rates varying accordingly.

Is data labeling a good career?

Data labeling is a growing field that involves annotating data for machine learning models, often requiring attention to detail and familiarity with tools like labeling platforms. It can offer flexible schedules and entry-level opportunities, but typically provides lower pay compared to other tech roles and may lack long-term career advancement without additional skills. Overall, it can be a suitable starting point for those interested in AI and data science, but may not be ideal as a long-term career without further development.

What are data labeling jobs?

Data labeling jobs involve annotating or tagging data such as images, text, or videos to help train machine learning models. These roles typically require attention to detail and familiarity with labeling tools or software, and may be performed remotely or in a team environment.

What are the most commonly searched types of Data Labeling jobs in Maryland?

The most popular types of Data Labeling jobs in Maryland are:

What are popular job titles related to Data Labeling jobs in Maryland?

For Data Labeling jobs in Maryland, the most frequently searched job titles are:

What job categories do people searching Data Labeling jobs in Maryland look for?

The top searched job categories for Data Labeling jobs in Maryland are:

What cities in Maryland are hiring for Data Labeling jobs?

Cities in Maryland with the most Data Labeling job openings:

Data Labeling jobs near you
Infographic showing various Data Labeling job openings in Maryland as of August 2026, with employment types broken down into 62% Full Time, 19% Part Time, and 19% Contract. Highlights an 100% In-person job distribution, with an average salary of $50,815 per year, or $24.4 per hour.

Clinical Data Scientist, FDA (Mid)

DRT Strategies, Inc.

Silver Spring, MD • On-site

$95K - $125K/yr

Full-time

Re-posted 19 days ago

Job description

Overview
DRT Strategies delivers expert management consulting and information technology (IT) solutions to large federal agencies, state and local government and commercial clients in health care, technology, and financial services industries.
The three letters of our name, DRT, stand for Driving Resolution Together, which is the core philosophy on which the company was founded. That is, we collaborate with our clients to solve their most pressing challenges - together.
We are problem solvers dedicated to your success, combining Fortune 500 experience with small business responsiveness. We have established a reputation with our clients as a forward-thinking consulting firm with demonstrated success in implementing solutions that lead to meaningful results. Our world-class consultants unite people to work collaboratively to achieve project goals and make vision a reality.
Project Description:
The Clinical Analyst contractor position provides scientific and clinical analytical support to CDER Office of New Drugs (OND) multi-disciplinary review teams. The individual will assist in the evaluation of drug applications, review clinical safety data, labeling assessment, and preparation of scientific reports. The work requires advanced knowledge in health and data sciences and
the ability to apply scientific expertise to support risk determinations in the context of regulatory review.
Note: This is a support role. All regulatory decisions, final recommendations, and official communications with applicants remain the exclusive responsibility of qualified FDA
federal employees. The contractor's work products are subject to review and approval by FDA staff.
Job Summary:
The Clinical Analyst position interacts with many FDA stakeholders across several Offices and Centers specifically with clinical reviewers (Medical Officers) and statistical reviewers. This role will be responsible for reviewing safety data sufficiency and integrity, conducting safety data analyses, verifying safety data submitted by the applicant, and generating high-quality scientific reports.
Responsibilities:
Clinical Data Analysis and Review
  • Analyze and evaluate submitted data from applicants seeking permission to market new drugs for general use and prepare analytical summaries on the adequacy of safety data provided.
  • Review NDAs, BLAs, supplements, and amendments; prepare draft analytical reports and recommendations for FDA reviewer consideration.
  • Incorporate summaries from clinical safety data reviews as part of integrated multi- disciplinary assessments. Prepare, oversee, and maintain project schedules.
Labeling Review Support
  • Assist in the review of proposed drug labeling to assess whether safety claims are truthful and adequately supported.
  • Provide draft safety data analysis on labeling accuracy and completeness for review by FDA staff.
Scientific Correspondence and Reporting
  • Draft scientifically sufficient reports of findings that clearly communicate clinical safety analyses and conclusions.
  • Prepare draft correspondence identifying facts and information inadequately presented in sponsor submissions, for FDA reviewer finalization and issuance.
  • Prepare clear summaries of clinical safety data tables, figures and listings for FDA review team use.
Literature Review and Knowledge Management
  • Review scientific literature and maintain awareness of current clinical developments and evolving findings in relevant therapeutic areas.
  • Support preparation of background materials for seminars, conferences, and industry meetings.
  • Stakeholder Support
  • Support clinical review teams in preparing for meetings with drug company representatives, advisory committees, and external scientific bodies.
Other Tasks
  • Lead meetings with clinical reviewers and statistical reviewers to present results from data quality assessments and standard safety data analyses.
  • Collaborate with CDER OND staff to optimize team processes and deliverables.
  • Work with FDA stakeholders to review background packages and mock safety datasets to assess appropriateness of controlled terminology and safety dataset structure.
  • Interact with government and contractor teams to help manage and monitor project progress, risk, issues, and track action items.
  • Manage, organize, and update SharePoint sites.
  • Assist in overall project support, as needed.
  • Support any other DRT tasks as assigned/requested by Portfolio Manager and Account Lead.
Required Experience:
  • PhD or PharmD with minimum of 3 years professional experience.
  • Technical proficiency in programming languages- R (mandatory) with demonstrated experience using R for data manipulation, analysis, and visualization in a clinical or regulatory research context.
  • R programming – ability to troubleshoot errors in R.
  • Experience with CDISC data standards (including SDTM and ADaM) and safety dataset structure (e.g., adsl.xpt, adae.xpt, adlb.xpt, advs.xpt, and adeg.xpt)
  • Understands data analytical methods (e.g., longitudinal analysis, time-to-event analyses, and causal/correlation analyses) for conducting safety data analyses (tables and figures)
  • Understands safety review elements including trial design, demographics, exposure, death, discontinuation, dose modification, SAE, TEAE, FMQ, AESI, laboratory tests, and vital signs. Working knowledge of safety analysis methods, including the evaluation of adverse event data, safety signal detection, and the preparation of standardized safety tables and figures.
  • Strong analytical and statistical skills to assess safety data.
  • Excellent organizational, time management, verbal and written communication skills.
  • Ability to independently manage a variety of projects with frequent interruptions and shifting priorities.
  • Ability to organize a continuous flow of work in a timely manner and meet mandatory deadlines.
  • Computer skills: MS Office Suite (particularly PowerPoint, Word, Excel), Adobe Acrobat.
  • Ability to work independently within a multidisciplinary team.
Preferred Experience:
  • Proficiency in manipulating data using R programming.
  • Experience and/or knowledge of analytical software including JReview, JMP, JMP Clinical, etc.
  • Experience in SAS programming.
  • Ability to apply knowledge of scientific research principles, study design concepts, and methods sufficient to evaluate clinical drug development programs.
  • Experience in applying clinical safety data analytical skills, including the ability to synthesize clinical and scientific evidence to inform risk assessments.
  • Experience in clinical trials, especially statistical hypothesis testing methods. Understands general concept of clinical trial design and drug development (e.g., adequate and well-controlled studies).
  • Statistical background, including experience with biostatistical methods commonly
    applied in clinical trial design, analysis, and interpretation (e.g., survival analysis, mixed-
    effects models, hypothesis testing).
  • Machine learning and AI background, including familiarity with predictive modeling
    techniques (e.g., classification models, regression models, random forest, or neural
    networks) and their potential applications in drug safety evaluation and regulatory science.
  • Epidemiological background, including experience with observational study design, real-
    world evidence, pharmacoepidemiology, or population-level safety surveillance methods.
  • Ability to work with little direct supervision on loosely defined tasks and coordinate work across multiple projects.
  • Experience identifying, articulating, and resolving complex, unique, and previously unresolved.
  • Familiarity with FDA regulatory process and/or working experience at FDA.
Education & Training:
  • PhD/PharmD or equivalent degrees are preferred.
Work Authorization, Clearance Requirement, & Additional Information:
  • This position requires the ability to obtain and maintain a U.S. government Public Trust clearance. Due to contract requirements, candidates must be U.S. citizens or lawful permanent residents (green card holders) to be eligible.
  • No agencies, third parties, or Corp-to-Corp submissions.
Salary Range:
  • $95,000-125,000
  • Salary commensurate with experience.

DRT Strategies, Inc. (DRT) follows the guidelines outlined by the Equal Employment Opportunity Commission (EEOC) to provide all employees and qualified applicants employment without regard to race, color, religion, sex (including pregnancy, childbirth, or related conditions, transgender status, and sexual orientation), national origin, age, genetic information, disability, protected veteran status, or any other protected characteristic under federal, state, or local law.

Reasonable accommodations for applicants and employees with disabilities will be provided. If a reasonable accommodation is needed to participate in the job application or interview process, to perform essential job functions, and/or to receive other benefits and privileges of employment, please contact Human Resources by emailing HR@drtstrategies.com, or by dialing 571-482-2517.

For additional information, please review the Know Your Rights: Workplace Discrimination is Illegal, E-Verify (English), E-Verify (Spanish). Right to Work (English), Right to Work (Spanish).

Please be aware of recruitment fraud where malicious individuals might pose as DRT Strategies. Only job postings and emails from drtstrategies.com are authentic and legitimate communications regarding DRT Strategies employment opportunities. Please contact Human Resources at hr@drtstrategies.com if you believe you have received a fraudulent email.

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