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Data Annotation Manager Jobs in Maryland (NOW HIRING)

With experts in biomedical science, software engineering, and program management, we focus on ... annotation, DEG), and Digital Spatial Profiling (annotation, QC, normalization, spatial ...

Data Scientist 3

Annapolis, MD · On-site

$161K - $211K/yr

... annotation of language data with parts of speech information, and improve existing models by ... Data Processing: (Data management and curation, data description and visualization, workflow and ...

Data Scientist 3

Annapolis, MD · On-site

$161K - $211K/yr

... annotation of language data with parts of speech information, and improve existing models by ... Data Processing: (Data management and curation, data description and visualization, workflow and ...

Department of Health and Human Services (DHHS) agencies to develop data science solutions to ... Experience in content development and/or text annotation (e.g., annotation of certain types of ...

Department of Health and Human Services (DHHS) agencies to develop data science solutions to ... Experience in content development and/or text annotation (e.g., annotation of certain types of ...

... annotation tools and semantic frameworks. Ability to clean and process large amounts of real-world ... Experience with database management systems (e.g., PostgresSQL, MySQL, SQLite, SQL, etc.) Excellent ...

... annotation tools and semantic frameworks. Ability to clean and process large amounts of real-world ... Experience with database management systems (e.g., PostgresSQL, MySQL, SQLite, SQL, etc.) Excellent ...

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Showing results 1-20

Data Annotation Manager information

See Maryland salary details

$30.1K

$94.3K

$166.9K

How much do data annotation manager jobs pay per year?

As of Sep 7, 2026, the average yearly pay for data annotation manager in Maryland is $94,283.00, according to ZipRecruiter salary data. Most workers in this role earn between $64,100.00 and $121,800.00 per year, depending on experience, location, and employer.

What does a data annotation manager do?

A Data Annotation Manager oversees the process of labeling and categorizing data used to train machine learning models. They manage teams of annotators, ensure data quality, develop annotation guidelines, and coordinate with data scientists to meet project requirements. Their role is critical in maintaining high standards of accuracy and efficiency, as well as ensuring that datasets are properly prepared for AI and machine learning applications.

What are the key skills and qualifications needed to thrive as a data annotation manager?

To thrive as a Data Annotation Manager, you need expertise in data labeling processes, quality control, and a solid understanding of machine learning concepts, usually backed by a degree in computer science or a related field. Proficiency with annotation tools such as Labelbox, Supervisely, or CVAT, as well as experience with project management systems, is commonly required. Exceptional leadership, attention to detail, and strong communication skills help manage teams and ensure high annotation accuracy. These skills are critical for delivering reliable labeled datasets, which are essential for building effective AI and machine learning models.

What are some common challenges faced by data annotation managers, and how can they be addressed?

Data Annotation Managers often encounter challenges such as maintaining high annotation quality across large and diverse datasets, managing a distributed team of annotators, and meeting tight project deadlines. To address these, it's important to implement robust quality assurance processes, provide ongoing training for annotators, and establish clear communication channels. Leveraging annotation tools with built-in validation features can also help ensure consistency and accuracy. Building a positive and collaborative team environment further contributes to better outcomes and workflow efficiency.

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

AspectData Annotation ManagerData Labeling Specialist
CredentialsBachelor's degree in related field, experience in data managementHigh school diploma or equivalent, training in labeling tools
Work EnvironmentTeam management, project oversight, collaboration with data scientistsHands-on labeling work, using annotation tools, focused on data tagging
Industry UsageUsed in AI/ML projects for overseeing annotation teamsPerforms the actual data labeling tasks in machine learning workflows

The Data Annotation Manager oversees the entire annotation process, managing teams and ensuring quality, while the Data Labeling Specialist focuses on executing labeling tasks. Both roles are essential in AI/ML data preparation but differ in responsibilities and scope.

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

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

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

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

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

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

What cities in Maryland are hiring for Data Annotation Manager jobs?

Cities in Maryland with the most Data Annotation Manager job openings:

Infographic showing various Data Annotation Manager job openings in Maryland as of August 2026, with employment types broken down into 87% Full Time, 6% Part Time, and 7% Contract. Highlights an 77% In-person, 6% Hybrid, and 17% Remote job distribution, with an average salary of $94,283 per year, or $45.3 per hour.

Data Scientist II

Axle

Rockville, MD

Full-time

Medical, Dental, Vision, Retirement, PTO

Re-posted 8 days ago


Job description

(ID: 2026-2574)

Axle is a bioscience and information technology company that offers advancements in translational research, biomedical informatics, and data science applications to research centers and healthcare organizations nationally and abroad. With experts in biomedical science, software engineering, and program management, we focus on developing and applying research tools and techniques to empower decision-making and accelerate research discoveries. We work with some of the top research organizations and facilities in the country including multiple institutes at the National Institutes of Health (NIH).

Benefits We Offer:

  • 100% Medical, Dental & Vision Coverage for Employees
  • Paid Time Off and Paid Holidays
  • 401K match up to 5%
  • Educational Benefits for Career Growth
  • Employee Referral Bonus
  • Flexible Spending Accounts:
    • Healthcare (FSA)
    • Parking Reimbursement Account (PRK)
    • Dependent Care Assistant Program (DCAP)
    • Transportation Reimbursement Account (TRN)

We are seeking a Data Scientist II to join our vibrant team supporting the National Cancer Institute (NCI) at the NIH in Rockville, MD. This role is embedded within NCI's Center for Biomedical Informatics and Information Technology (CBIIT), where you will directly advance cancer research by building the computational infrastructure that scientists depend on every day.

You will support the full omics data lifecycle across a broad spectrum of modalities, including bulk RNA-seq, single-cell RNA-seq (scRNA-seq), spatial transcriptomics, Digital Spatial Profiling (DSP), whole genome and exome sequencing (WGS/WES), metagenomics, metabolomics, and proteomics, as well as clinical, imaging, and biospecimen data. A core part of this role involves developing workflows that integrate these modalities to support systems-level biological questions, cross-cohort studies, and NCI CBIIT initiatives.

You will collaborate closely with NCI scientists, bioinformaticians, clinician-researchers, data engineers, software developers, and government stakeholders to ensure analytical infrastructure is FAIR-compliant, containerized, version-controlled, well-documented, and purpose-built for long-term reuse across the research community.

Key Responsibilities

  • Bioinformatics Workflow and Data Pipeline Development: Design, build, and maintain reproducible pipelines for diverse biomedical data types — including genomic, transcriptomic, single-cell, spatial, proteomic, metagenomic, metabolomic, and clinical datasets. Develop reusable transformation logic and curated datasets supporting analytics, dashboards, APIs, notebooks, and downstream research workflows.

  • Multi-Omics Analysis: Support NCI CBIIT labs in their analysis workflows including bulk RNA-seq (QC, DEG, GSEA), single-cell RNA-seq (clustering, UMAP/t-SNE, cell type annotation, DEG), and Digital Spatial Profiling (annotation, QC, normalization, spatial deconvolution, volcano plots, heatmaps).

  • Data Integration and Lifecycle Support: Enable reliable data movement from source systems into structured, analysis-ready formats. Support ingestion, curation, metadata capture, source-to-target mapping, schema management, provenance tracking, and long-term maintainability of data products.

  • Statistical Modeling and Machine Learning: Apply statistical and ML methods — including hypothesis testing, regression, clustering, PCA, UMAP, t-SNE, and classification — to biomedical datasets. Incorporate AI/LLM-based extraction where appropriate, with clear validation and communication to stakeholders.

  • Researcher-Facing Applications and Visualization: Build and support interactive dashboards (Shiny, Streamlit), notebooks, reports, and APIs enabling researchers to explore multi-omics and clinical data. Support figure generation for QC, differential expression, pathway, and spatial analyses.

  • Collaboration: Partner with data scientists, bioinformaticians, researchers, developers, and government stakeholders to translate scientific needs into technical specifications, data models, and reusable workflows that accelerate biomedical research.

Required Qualifications

  • Education & Background: Bachelor's degree in Data Science, Bioinformatics, Computer Science, Biological Sciences, or a related field (advanced degree preferred), or equivalent experience. Demonstrated experience in a data-intensive role supporting biomedical research or scientific computing.

  • Data Science and Bioinformatics Expertise: Strong proficiency in Python and R for analysis, scripting, and visualization. Hands-on experience with at least two omics data types (e.g., bulk RNA-seq, scRNA-seq, spatial transcriptomics, proteomics, metagenomics, GWAS).

  • Analytical Skills: Solid understanding of statistical modeling, dimensionality reduction, clustering, differential expression, and pathway analysis. Ability to work with structured, semi-structured, and unstructured data across relational and data lake environments.

  • Collaboration & Communication: Strong problem-solving skills with the ability to communicate effectively across technical and non-technical audiences. Able to translate scientific needs into technical solutions and clearly articulate risks, assumptions, and limitations.

  • Domain Alignment: Genuine interest in biomedical and translational research. Ability to quickly learn domain-specific terminology and workflows, with awareness of data governance, privacy, and compliance requirements for clinical and research data.

Preferred Qualifications

  • Data Platform Experience: Experience building analytics solutions in platforms such as Snowflake, Databricks, or cloud data warehouses, with integrations across databases, APIs, dashboards, and application environments.

  • Bioinformatics Workflow Tooling: Experience with workflow and reproducibility tools used in Galaxy, Terra, Nextflow/WDL, Snakemake, Singularity, or CWL. Familiarity with the scverse Python ecosystem (Scanpy, Squidpy, SCIMAP, AnnData) and spatial single-cell analysis methods, including PhenoGraph, Louvain/Leiden clustering, UMAP, and Ripley's L statistic, is a plus.

  • Research and Application Enablement: Experience preparing curated datasets for dashboards, APIs, and web applications. Familiarity with Posit Connect, R/Shiny, Streamlit, Jupyter, or similar platforms is a plus.

  • Cloud, HPC, Storage, and Automation: Experience with AWS (EC2, S3, Lambda), object storage, relational databases, scheduled jobs, API integrations, and secure data movement. Familiarity with HPC environments, SLURM/SGE, or NIH Biowulf is preferred.

  • Biomedical Domain Knowledge: Background in biomedical research, clinical research, or healthcare analytics. Familiarity with standards such as HL7/FHIR, CDISC, or OMOP, and experience with clinical, genomic, or biospecimen data is a plus.

  • Governance and Reproducibility: Experience with metadata management, data lineage, open-source code release, containerized analyses, and secure handling of de-identified or access-controlled research datasets.

  • Training and Scientific Enablement: Experience creating documentation, training materials, or workshops for researchers and non-coder audiences. Ability to support tool adoption and explain workflows and results clearly is strongly preferred.

Disclaimer: The above description is meant to illustrate the general nature of work and level of effort being performed by individuals assigned to this position or job description. This is not restricted as a complete list of all skills, responsibilities, duties, and/or assignments required. Individuals may be required to perform duties outside of their position, job description or responsibilities as needed.

The diversity of Axle's employees is a tremendous asset. We are firmly committed to providing equal opportunity in all aspects of employment and will not tolerate any illegal discrimination or harassment based on age, race, gender, religion, national origin, disability, marital status, covered veteran status, sexual orientation, status with respect to public assistance, and other characteristics protected under state, federal, or local law and to deter those who aid, abet, or induce discrimination or coerce others to discriminate.

Accessibility: If you need an accommodation as part of the employment process please contact: careers@axleinfo.com

This role has a market-competitive salary with an anticipated base compensation range listed below. Actual salaries will vary depending on a candidate's experience, qualifications, skills, and location.

Disclaimer: The above description is meant to illustrate the general nature of work and level of effort being performed by individuals assigned to this position or job description. This is not restricted as a complete list of all skills, responsibilities, duties, and/or assignments required. Individuals may be required to perform duties outside of their position, job description or responsibilities as needed.

The diversity of Axle's employees is a tremendous asset. We are firmly committed to providing equal opportunity in all aspects of employment and will not tolerate any illegal discrimination or harassment based on age, race, gender, religion, national origin, disability, marital status, covered veteran status, sexual orientation, status with respect to public assistance, and other characteristics protected under state, federal, or local law and to deter those who aid, abet, or induce discrimination or coerce others to discriminate.

Accessibility: If you need an accommodation as part of the employment process please contact: careers@axleinfo.com

This role has a market-competitive salary with an anticipated base compensation range listed below. Actual salaries will vary depending on a candidate's experience, qualifications, skills, and location.

#IND

Salary Range
$130,000—$145,000 USD