ZipRecruiter

Log In

1

Data Annotation Tech Jobs in Brooklyn, NY (NOW HIRING)

Smarsh

Lead Data Scientist

New York, NY · On-site +1

Data annotation and quality review * Exploratory data analysis and model fail state analysis ... We use the latest in AI/ML technology to help our customers break new ground at scale. We are a ...

Smarsh

Lead Data Scientist

New York, NY

Data annotation and quality review * Exploratory data analysis and model fail state analysis ... We use the latest in AI/ML technology to help our customers break new ground at scale. We are a ...

Alice

Freelancer - GenAI Experts

New York, NY · Remote

The most sophisticated technology challenges in the world are waiting down the rabbit hole. We're ... Support data annotation, curation, and quality control processes * Summarize findings into ...

Alice

Freelancer - GenAI Experts

New York, NY · On-site +1

The most sophisticated technology challenges in the world are waiting down the rabbit hole. We're ... Support data annotation, curation, and quality control processes * Summarize findings into ...

Stackruit Ltd.

Senior AI Dataset Engineer

Manhattan, NY · On-site

$120 - $150/hr

Our proactive technology protects against current and future threats, making us the top choice for ... Execute data generation, cleaning, annotation, and new dataset construction. * Auto-Annotation ...

previous pagenext page

Showing results 21-40

All JobsData Annotation Tech JobsData Annotation Tech Jobs in Brooklyn, NY

Data Annotation Tech information

See Brooklyn, NY salary details

$12

$24

$36

How much do data annotation tech jobs pay per hour?

As of Aug 16, 2026, the average hourly pay for data annotation tech in Brooklyn, NY is $24.02, according to ZipRecruiter salary data. Most workers in this role earn between $17.69 and $28.56 per hour, depending on experience, location, and employer.

How to become a data annotation tech?

To become a data annotation tech, you typically need a high school diploma or equivalent, strong attention to detail, and familiarity with data labeling tools or software. Some roles may require basic knowledge of machine learning concepts or specific platforms like Labelbox or CVAT. Gaining experience through online courses or tutorials can also improve job prospects.

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

To thrive as a Data Annotation Tech, you need keen attention to detail, basic computer literacy, and familiarity with data labeling standards, often supported by a high school diploma or equivalent. Experience with annotation platforms, image or text labeling tools, and basic knowledge of data management systems is highly valuable. Strong organizational skills, patience, and effective communication set top candidates apart in this field. These skills and qualities ensure annotated data is accurate, consistent, and valuable for machine learning or AI projects.

What does a data annotation tech do?

A typical day as a Data Annotation Tech involves reviewing large sets of data—such as images, text, or audio—and accurately labeling or categorizing them using specialized software. You may work independently or as part of a team, following specific project guidelines to ensure data integrity and consistency. Collaboration with project managers or data scientists is common when clarifying ambiguous data points or addressing annotation challenges. Additionally, productivity targets and quality checks are a regular part of the workflow, helping to keep projects on schedule and maintain high standards.

Does data annotation actually pay you?

Data annotation jobs typically pay hourly or per task, with rates varying depending on the platform and complexity of the work. Many companies and platforms offer remote opportunities that provide consistent payment once tasks are completed, often requiring basic skills in data labeling tools. Payment is generally reliable for those who meet quality standards and adhere to deadlines.

What is a data annotation tech?

A Data Annotation Tech is responsible for labeling and categorizing data, such as text, images, audio, or video, to train machine learning models. They follow specific guidelines to ensure accuracy and consistency in annotations, which helps improve the performance of AI systems. This role often involves repetitive tasks, attention to detail, and familiarity with various annotation tools. Data annotation is crucial for AI development in industries like healthcare, finance, and autonomous driving.

How much do data annotation technicians make?

Data annotation technicians typically earn between $12 and $20 per hour, depending on experience, location, and the complexity of the tasks. Many roles are entry-level and may offer flexible schedules or remote work opportunities.

What are the most commonly searched types of Data Annotation Tech jobs in Brooklyn, NY?

The most popular types of Data Annotation Tech jobs in Brooklyn, NY are:

What are popular job titles related to Data Annotation Tech jobs in Brooklyn, NY?

For Data Annotation Tech jobs in Brooklyn, NY, the most frequently searched job titles are:

What job categories do people searching Data Annotation Tech jobs in Brooklyn, NY look for?

The top searched job categories for Data Annotation Tech jobs in Brooklyn, NY are:

What cities near Brooklyn, NY are hiring for Data Annotation Tech jobs?

Cities near Brooklyn, NY with the most Data Annotation Tech job openings:

Data Annotation Tech jobs near you
Infographic showing various Data Annotation Tech job openings in Brooklyn, NY as of August 2026, with employment types broken down into 1% As Needed, 87% Full Time, 9% Part Time, and 3% Contract. Highlights an 84% Physical, 4% Hybrid, and 12% Remote job distribution, with an average salary of $49,959 per year, or $24 per hour.

Lead Data Scientist

Smarsh

New York, NY • On-site, Remote

Full-time

Re-posted 9 hours ago

Job description

Who are we?
Smarsh empowers its customers to manage risk and unleash intelligence in their digital communications. Our growing community of over 6500 organizations in regulated industries counts on Smarsh every day to help them spot compliance, legal or reputational risks in 80+ communication channels before those risks become regulatory fines or headlines. Relentless innovation has fueled our journey to consistent leadership recognition from analysts like Gartner and Forrester, and our sustained, aggressive growth has landed Smarsh in the annual Inc. 5000 list of fastest-growing American companies since 2008.
Summary
As a Lead Data Scientist (NLP & Financial Compliance) at Smarsh, you will spearhead the development of state-of-the-art natural language processing (NLP) and large language model (LLM) solutions that power next-generation compliance and surveillance systems. You'll work on highly specialized problems at the intersection of natural language processing, communications intelligence, financial supervision, and regulatory compliance, where unstructured data from emails, chats, voice transcripts, and trade communications hold the keys to uncovering misconduct and risk.
The role will involve working with other Senior Data Scientists and mentoring Associate Data Scientists in analyzing complex data, generating insights, and creating solutions as needed across a variety of tools and platforms. This role demands both technical excellence in NLP modeling and a deep understanding of financial domain behavior-including insider trading, market manipulation, off-channel communications, MNPI, bribery, and other supervisory risk areas. The ideal candidate for this position will possess the ability to perform both independent and team-based research and generate insights from large data sets with a hands-on/can do attitude of servicing/managing day to day data requests and analysis.
This role also offers a unique opportunity to get exposure to many problems and solutions associated with taking machine learning and analytics research to production. On any given day, you will have the opportunity to interface with business leaders, machine learning researchers, data engineers, platform engineers, data scientists and many more, enabling you to level up in true end-to-end data science proficiency.
How will you contribute?
  • Collect, analyze, and interpret small/large datasets to uncover meaningful insights to support the development of statistical methods / machine learning algorithms.
  • Lead the design, training, and deployment of NLP and transformer-based models for financial surveillance and supervisory use cases (e.g., misconduct detection, market abuse, trade manipulation, insider communication).
  • Development of machine learning models and other analytics following established workflows, while also looking for optimization and improvement opportunities
  • Data annotation and quality review
  • Exploratory data analysis and model fail state analysis
  • Contribute to model governance, documentation, and explainability frameworks aligned with internal and regulatory AI standards.
  • Client/prospect guidance in machine learning model and analytic fine-tuning/development processes
  • Provide guidance to junior team members on model development and EDA
  • Work with Product Manager(s) to intake project/product requirements and translate these to technical tasks within the team's tooling, technique and procedures
  • Continued self-led personal development

What will you bring?
  • Strong understanding of financial markets, compliance, surveillance, supervision, or regulatory technology
  • Experience with one or more data science and machine/deep learning frameworks and tooling, including scikit-learn, H2O, keras, pytorch, tensorflow, pandas, numpy, carot, tidyverse
  • Command of data science and statistics principles (regression, Bayes, time series, clustering, P/R, AUROC, exploratory data analysis etc...)
  • Strong knowledge of key programming concepts (e.g. split-apply-combine, data structures, object-oriented programming)
  • Solid statistics knowledge (hypothesis testing, ANOVA, chi-square tests, etc...)
  • Knowledge of NLP transfer learning, including word embedding models (gloVe, fastText, word2vec) and transformer models (Bert, SBert, HuggingFace, and GPT-x etc.)
  • Experience with natural language processing toolkits like NLTK, spaCy, Nvidia NeMo
  • Knowledge of microservices architecture and continuous delivery concepts in machine learning and related technologies such as helm, Docker and Kubernetes
  • Familiarity with Deep Learning techniques for NLP.
  • Familiarity with LLMs - using ollama & Langchain
  • Excellent verbal and written skills
  • Proven collaborator, thriving on teamwork

Preferred Qualifications
  • Master's or Doctor of Philosophy degree in Computer Science, Applied Math, Statistics, or a scientific field
  • Familiarity with cloud computing platforms (AWS, GCS, Azure)
  • Experience with automated supervision/surveillance/compliance tools

$166,000 - $214,000 a year
The above salary range represents Smarsh's good faith and reasonable estimate of the range of possible base compensation at the time of posting. Any applicable bonus programs will be discussed during the recruiting process.
The salary for this role will be set based on a variety of factors, including but not limited to, internal equity, experience, education, location, specialty and training.
Local cost of living assessments are done for each new hire at the time of offer.
About our culture
Smarsh hires lifelong learners with a passion for innovating with purpose, humility and humor. Collaboration is at the heart of everything we do. We work closely with the most popular communications platforms and the world's leading cloud infrastructure platforms. We use the latest in AI/ML technology to help our customers break new ground at scale. We are a global organization that values diversity, and we believe that providing opportunities for everyone to be their authentic self is key to our success. Smarsh leadership, culture, and commitment to developing our people have all garnered Comparably.com Best Places to Work Awards. Come join us and find out what the best work of your career looks like.

Apply