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Data Annotation Manager Jobs in New Jersey (NOW HIRING)

AmNet Services, Inc.

NJ · On-site

... management space. We design and manufacture sensors for storage tanks, water metering, energy ... Whether an off-the-shelf or custom solution is needed, we'll create a solution and push the data on ...

MJH Life Sciences

Principal Medical Writer

Cranbury, NJ · On-site

... managing writers and freelancers, interfacing with clients and key opinion leaders, and ensuring ... data, adhering to appropriate reference and annotation standards * Review and edit content at ...

MJH Life Sciences

Principal Medical Writer

Cranbury, NJ · On-site

$95K - $105K/yr

... data. * Adhere to appropriate reference and annotation standards and resolving fact-check queries ... Collaborate with project management and creative services to ensure that revisions and appropriate ...

All JobsData Annotation JobsData Annotation Manager JobsData Annotation Manager Jobs in New Jersey

Data Annotation Manager information

See New Jersey salary details

$31.5K

$98.6K

$174.6K

How much do data annotation manager jobs pay per year?

As of Aug 1, 2026, the average yearly pay for data annotation manager in New Jersey is $98,625.00, according to ZipRecruiter salary data. Most workers in this role earn between $67,000.00 and $127,400.00 per year, depending on experience, location, and employer.

What is the salary of data annotation manager?

The salary of a data annotation manager typically ranges from $60,000 to $120,000 annually, depending on experience, location, and company size. Senior roles or those in high-cost areas may offer higher compensation, and familiarity with annotation tools and team management can influence pay levels.

How much do data annotation project managers make?

Data annotation project managers typically earn between $60,000 and $100,000 annually, depending on experience, location, and company size. They oversee annotation teams, coordinate workflows, and ensure quality standards are met, often requiring familiarity with annotation tools and project management skills.

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 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.

Does data annotation actually pay well?

Data annotation managers typically earn competitive salaries that reflect their experience and responsibilities, often ranging from entry-level to senior roles. Compensation can vary based on industry, location, and company size, with specialized skills in tools like labeling platforms and quality control often leading to higher pay.

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

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.

How hard is it to get hired by data annotation?

Getting hired as a data annotation manager typically requires relevant experience in data labeling, familiarity with annotation tools, and strong organizational skills. The hiring process often involves reviewing previous work, technical assessments, and demonstrating attention to detail, with opportunities available in companies that outsource data labeling tasks.

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 New Jersey? The most popular types of Data Annotation jobs in New Jersey are:
What are popular job titles related to Data Annotation Manager jobs in New Jersey? For Data Annotation Manager jobs in New Jersey, the most frequently searched job titles are:
What job categories do people searching Data Annotation Manager jobs in New Jersey look for? The top searched job categories for Data Annotation Manager jobs in New Jersey are:
What cities in New Jersey are hiring for Data Annotation Manager jobs? Cities in New Jersey with the most Data Annotation Manager job openings:
Data Annotation Manager jobs near you
Infographic showing various Data Annotation Manager job openings in New Jersey as of July 2026, with employment types broken down into 57% Full Time, 14% Part Time, and 29% Contract. Highlights an 100% In-person job distribution, with an average salary of $98,625 per year, or $47.4 per hour.

Senior Data Management Professional - Data Engineering (Data AI)

Bloomberg LP

Princeton, NJ • On-site

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 17 days ago

Bloomberg rating

9.4

Company rating: 9.4 out of 10

Based on 11 frontline employees who took The Breakroom Quiz

12th of 241 rated software companies

Job description

Senior Data Management Professional - Data Engineering (Data AI)
Location
Princeton
Business Area
Data
Ref #
10052626
Description & Requirements
Bloomberg runs on data. Our products are fueled by powerful information. We combine data and context to paint the whole picture for our clients, around the clock - from around the world. In Data, we are responsible for delivering this data, news, and analytics through innovative technology - quickly and accurately. We apply problem-solving skills to identify workflow efficiencies and implement technology solutions to enhance our systems, products, and processes.
Our Team:
Data AI contributes to the building of Bloomberg's AI-enhanced products at scale by curating model training data and enhancing how our internal processes use AI. We provide evaluation and annotation frameworks connecting natural language processing and human judgment in order to elevate the quality, intelligence, and usability of the data that drives our products.
By investing in AI at a strategic level, we expand our practice of engaging with AI to one that is embedded across Data. Our internal processes to take advantage of new AI technologies and strengthen Data's role in providing robust domain expertise and influential data artifacts to Bloomberg's products. As a result our clients will continue to have high quality data and access to new types of datasets.
The Role:
As a Data Engineer within Data AI, you will build and evolve the infrastructure, data pipelines, and operational tooling that power scalable AI and data workflows. You will enable reliable data collection, annotation, training, and evaluation processes by developing systems that improve data quality, operational visibility, and workflow efficiency. Through automation, observability, and platform engineering, you will help create the foundations that allow teams to deliver data and AI products with confidence and at scale.
We'll trust you to:
  • Design, build, and maintain scalable data pipelines that support data collection, annotation, training, evaluation, analytics, and reporting workflows.
  • Develop and operate systems for dataset management, storage, versioning, and lifecycle governance to ensure reliable and reproducible AI workflows.
  • Implement monitoring, observability, and alerting capabilities that provide visibility into data quality, system health, and operational performance.
  • Build dashboards, tooling, and self-service capabilities that improve transparency, efficiency, and decision-making across data operations.
  • Partner with Product, Engineering, and Data teams to evolve the infrastructure and platforms supporting AI-enabled products and workflows.
  • Identify bottlenecks and opportunities for automation, delivering scalable solutions that improve reliability, consistency, and operational efficiency.

You'll need to have:
  • Bachelor's degree in Finance, Business, Economics, Accounting, STEM or degree-equivalent qualifications
  • 3+ years in data engineering (Python, SQL)
  • Experience building ETL/data pipelines at scale and creating data collection frameworks for structured and unstructured data
  • Experience with data modeling and developing proactive data quality strategies that ensure data is fit for purpose
  • Experience working with ML/AI datasets or experimentation workflows.
  • Excellent problem-solving and analytical thinking skills with strong attention to detail.
  • Proven track record of stakeholder relationship management, communication, and cross-team collaboration.

We'd love to see:
  • Keen interest in and familiarity with generative AI frameworks and the requirements of Agentic AI.
  • Experience in semantic structures or large scale data modeling
  • Experience using data visualization tools such as Tableau, QlikSense, or PowerBI
  • Experience developing or managing annotation programs and training/evaluation datasets for ML or NLP models.
  • Deep domain expertise in financial markets/news and understanding of our customers' needs.

If this sounds like you:
Apply! If you think we're a good match. We'll get in touch to let you know the next steps!
Salary Range = 110,000 - 190,000 USD Annual + Benefits + Bonus
The referenced salary range is based on the Company's good faith belief at the time of posting. Actual compensation may vary based on factors such as geographic location, work experience, market conditions, education/training and skill level.
We offer one of the most comprehensive and generous benefits plans available and offer a range of total rewards that may include merit increases, incentive compensation (exempt roles only), paid holidays, paid time off, medical, dental, vision, short and long term disability benefits, 401(k) +match, life insurance, and various wellness programs, among others. The Company does not provide benefits directly to contingent workers/contractors and interns.
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About Bloomberg

Sourced by ZipRecruiter

Bloomberg runs on data. As the Data Management & Analytics team within Engineering, we support our organization's needs around managing data efficiently. The vision of the team is to build solutions that drive data quality, data dictionary, data stewardship, data lineage, reference, and master data management across various data domains (prospect, customer, vendor, material etc.). We partner with business teams across the organization in addressing their data needs and ultimately helping run business operations efficiently and make improved decisions.

Industry

Finance and insurance

Company size

10,000+ Employees

Headquarters location

New York, NY, US

Year founded

1981

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