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Data Labelling Jobs in Secaucus, NJ (NOW HIRING)

Middesk

Lead Data Scientist

New York, NY · On-site

$210K - $250K/yr

Middesk is building the data and intelligence infrastructure that helps businesses work together ... Innovate in feature engineering & labeling: Use graph-based techniques, weak supervision, LLMs, and ...

ITS Caliber Staffing

Data Entry Specialist

Manhattan, NY · On-site

$18.75 - $25/hr

About the job Data Entry Specialist We are an advanced manufacturer of pressure sensitive labels. Due to significant growth we are looking to expand our administrative team to include additional Data ...

Warner Records

Data Scientist

New York, NY · On-site

The role involves the Data Scientist partnering with various departments across Warner Music ... You will assist our labels and artists in connecting with consumers across diverse media and ...

SG Services, Inc.

Data Insights Manager

Brooklyn, NY · On-site

$65K/yr

Data Insights Manager Status: Full-time Hours: 40/week, Monday - Friday, 9 am-6 pm Location ... Create regular and ad-hoc reports to inform label partners, artists, and internal teams on key ...

SG Services, Inc.

Data Insights Manager

Brooklyn, NY · On-site

$65K/yr

Data Insights Manager Status: Full-time Hours: 40/week, Monday - Friday, 9 am-6 pm Location ... Create regular and ad-hoc reports to inform label partners, artists, and internal teams on key ...

SG Services, Inc.

Data Insights Manager

Brooklyn, NY · On-site

$65K/yr

Data Insights Manager Status: Full-time Hours: 40/week, Monday - Friday, 9 am-6 pm Location ... Create regular and ad-hoc reports to inform label partners, artists, and internal teams on key ...

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All JobsData Labelling JobsData Labelling Jobs in Secaucus, NJ

Data Labelling information

See Secaucus, NJ salary details

$46.8K

$167.8K

$247.6K

How much do data labelling jobs pay per year?

As of Aug 21, 2026, the average yearly pay for data labelling in Secaucus, NJ is $167,771.00, according to ZipRecruiter salary data. Most workers in this role earn between $135,700.00 and $172,800.00 per year, depending on experience, location, and employer.

What is a data labelling?

A Data Labelling job involves annotating data, such as text, images, audio, or video, to help train machine learning models. Labelers categorize or tag data by following specific guidelines to ensure accuracy and consistency. This process is essential for improving AI applications, including image recognition, natural language processing, and autonomous systems. Attention to detail and adherence to instructions are key skills required for this role.

What are the typical daily responsibilities of a data labelling professional?

Data Labelling professionals are generally responsible for reviewing and accurately annotating large volumes of data—such as images, audio, video, or text—to support machine learning and AI projects. This often involves using specialized labeling platforms and following detailed guidelines provided by data scientists or project managers. You may also participate in regular team meetings to discuss quality standards or address ambiguities in data, and your work is typically reviewed for accuracy before being integrated into training datasets. Collaborating with other data annotators, engineers, and analysts is a common part of the process to ensure consistency and high-quality results.

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

To thrive as a Data Labelling professional, you need strong attention to detail, proficiency with data annotation processes, and a basic understanding of machine learning concepts. Familiarity with annotation tools like Labelbox, Supervisely, or Amazon SageMaker Ground Truth is often required, and some roles may value certifications in data processing or AI fundamentals. Reliability, patience, and the ability to follow precise instructions are important soft skills for success in this position. These skills ensure accurate and consistent data labeling, which is critical for developing effective AI models and maintaining data integrity.

How can I get started in data labeling?

To start in data labeling, gain familiarity with annotation tools and understand the specific data types you'll work with, such as images, text, or audio. Building attention to detail and basic knowledge of machine learning concepts can improve your effectiveness; some roles may require basic computer skills or certifications. Entry-level positions often offer flexible schedules and remote work options.

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 they work for. Some may earn higher rates with specialized skills or certifications, especially for complex data annotation tasks involving images, videos, or audio. Pay can vary based on whether the work is freelance, part-time, or full-time, and some roles offer project-based or hourly compensation.

Is data labelling a good career?

Data labelling is a common entry-level role in data annotation and machine learning workflows, often requiring attention to detail and familiarity with labeling tools. It can provide opportunities to develop skills in data management and AI, but typically offers lower pay and limited advancement without additional training or experience.

What are data labeling jobs?

Data labeling jobs involve annotating or tagging data such as images, text, or videos to help machine learning models learn and improve. These roles typically require attention to detail and familiarity with labeling tools or software, and they are often performed remotely with flexible schedules.

What are the most commonly searched types of Data Labelling jobs in Secaucus, NJ?

The most popular types of Data Labelling jobs in Secaucus, NJ are:

What are popular job titles related to Data Labelling jobs in Secaucus, NJ?

For Data Labelling jobs in Secaucus, NJ, the most frequently searched job titles are:

What job categories do people searching Data Labelling jobs in Secaucus, NJ look for?

The top searched job categories for Data Labelling jobs in Secaucus, NJ are:

What cities near Secaucus, NJ are hiring for Data Labelling jobs?

Cities near Secaucus, NJ with the most Data Labelling job openings:

Data Labelling jobs near you
Infographic showing various Data Labelling job openings in Secaucus, NJ as of August 2026, with employment types broken down into 1% As Needed, 88% Full Time, 9% Part Time, and 2% Contract. Highlights an 85% Physical, 4% Hybrid, and 11% Remote job distribution, with an average salary of $167,771 per year, or $80.7 per hour.

Lead Data Scientist

Middesk

New York, NY • On-site

$210K - $250K/yr

Full-time

Re-posted 26 days ago

Job description

About Middesk:
Middesk is building the data and intelligence infrastructure that helps businesses work together with confidence. We started by creating a comprehensive platform for understanding businesses, bringing together authoritative and proprietary data to help customers verify business identities, onboard customers faster, and manage risk throughout the customer lifecycle.
Today, Middesk is used by more than 700 banks and fintechs, and in 2025 we verified more than 7 million companies. We've also expanded beyond business verification to help companies form, register, manage, and maintain their businesses, supporting more than 50,000 companies in setting up over 100,000 accounts required to hire employees, run payroll, and stay compliant.
Middesk came out of Y Combinator, and is backed by Sequoia Capital, Accel, Insight Partners, and Canapi. We're proud to be named on the Forbes Fintech 50 and Best Startup Employers lists.
About The Role:
We are actively building AI-driven applications that streamline customer workflows, focusing on business onboarding. With our proprietary identity data assets and deep domain expertise, we are uniquely positioned to expand into a broader set of AI-powered solutions that drive long-term growth.
We're looking for a hands-on applied ML expert to help build the technical foundation for these efforts. Ideally you have shipped external-facing models in the risk/fraud space and know the messy realities of imbalanced data, low labels, and changing behavior. This is a highly technical, hands-on role with wide influence on how we design, build, and scale ML at Middesk.
We follow a hybrid work model, and for this role, there is an expectation of 2 days per week in our SF/NYC office. Candidates should be based within a commutable distance, as we believe in the value of in-person collaboration and building strong team connections while also supporting flexibility where possible.
What You'll Do:
  • Build risk & fraud ML applications: Deliver production ML models in fraud, trust & safety, KYB, and compliance domains, with measurable impact on customer workflows.
  • Tackle hard data problems: Work on classification problems with extreme class imbalance, sparse signals, and "cold start" label challenges.
  • Innovate in feature engineering & labeling: Use graph-based techniques, weak supervision, LLMs, and AI agents to improve signal extraction and automate labeling process.
  • Establish ML infrastructure foundations: Partner with the ML infra team to design feature services, model training pipeline, model serving standards, and orchestration to scale multiple ML use cases.
  • Design and implement knowledge graph solutions: Leveraging LLMs for graph construction, querying, and retrieval to enhance entity resolution and business identity use cases.
What We're Looking For:
  • 7+ years of production ML experience in one or more of the following areas:
    • Building Production ML for risk, fraud, credit, or trust & safety: Track record of shipping external-facing ML applications in one or more of these domains.
    • Knowledge graph applications: Hands-on experience building, querying, or extracting signals from knowledge graphs-ideally over business entity networks (companies, persons, addresses, relationships) to support identity verification, fraud detection, or risk decisioning.
    • Entity resolution for business or individual identities: Experience disambiguating and linking records across noisy, incomplete, or conflicting data sources-particularly in KYB, KYC, AML, or identity verification contexts where the same real-world entity may appear under different names, addresses, or tax IDs.
  • Expertise in classification with real-world ML challenges, for example: imbalanced labels, sparse signals, cold start, and production version management.
  • Hands-on ML infrastructure experience: feature stores, model management, ML training/serving pipelines.
  • Comfort as a senior IC: setting technical direction, mentoring peers, and establishing best practices.

Nice-To Have:
  • B2B SaaS experience, ideally building ML products for enterprise customers.
  • ML pipeline and automation engineering: Experience building end-to-end training harnesses that automate feature engineering, data validation, and model training.
  • Experience scaling ML across multiple products or risk domains.

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