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Data Annotation Jobs in Georgia (NOW HIRING)

Smarsh

Lead Data Scientist

Atlanta, GA · On-site

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 ...

Smarsh

Lead Data Scientist

Atlanta, GA

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 ...

Artech

Data Architect : Atlanta, GA

Atlanta, GA · On-site

$65 - $70/hr

... annotation, and related data and analytics. They are experienced with Cloud native platforms such as AWS, Azure, Google, *** Cloud, or Cloud Native data platforms like Snowflake. Please apply on our ...

Navtech, Inc.

Sr. Apigee Developer

Atlanta, GA · On-site

... Data Formats (REST, JSON, SOAP & XML). * 3+ Experience in API management tools like Apigee Edge in ... Strong knowledge in API Modelling languages and annotation (YAML, Swagger, RAML) * Strong ...

4P Consulting Inc.

GIS Tech 1 or 2

Bainbridge, GA

... annotation, and symbology * Engage in storm restoration activities * Validate GIS model connectivity and correct connectivity issues * /Perform quality control and data validation activities

CNN

Applied AI Specialist

Atlanta, GA · On-site

... data, and quality guidance that shapes what the system produces • Work directly with editorial ... annotation tools, or human-in-the-loop methodologies • Experience working with cross-functional ...

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All JobsData Annotation JobsData Annotation Jobs in Georgia

Data Annotation information

See Georgia salary details

$7

$19

$42

How much do data annotation jobs pay per hour?

As of Jun 30, 2026, the average hourly pay for data annotation in Georgia is $19.63, according to ZipRecruiter salary data. Most workers in this role earn between $13.28 and $23.38 per hour, depending on experience, location, and employer.

Is data annotation a legitimate?

Data annotation is a legitimate job that involves labeling data such as images, text, or audio to help train machine learning models. It is commonly performed remotely and requires attention to detail, basic technical skills, and familiarity with annotation tools. Many companies hire data annotators as part of their AI development teams.

What does a typical workday look like for someone in a Data Annotation role?

A typical workday as a Data Annotator involves reviewing datasets—such as images, audio, text, or video—and accurately labeling or categorizing information according to specific project guidelines. Most Data Annotators work independently, but they often collaborate with project managers or data scientists to clarify requirements and resolve ambiguities. Tasks may be repetitive, but adhering to precise standards is vital for maintaining data quality. Work environments can range from technology companies to remote or freelance settings, and advancement opportunities exist as team leads or quality assurance specialists for those who excel in consistency and reliability.

What is a Data Annotation job?

A Data Annotation job involves labeling and categorizing data, such as text, images, audio, or video, to help train machine learning models. Annotators apply tags, bounding boxes, or classifications to data based on specific guidelines. This process improves the accuracy of AI systems in recognizing patterns and making predictions. Many data annotation jobs require attention to detail and familiarity with specific domains. It is commonly used in applications like autonomous driving, natural language processing, and computer vision.

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

To thrive in Data Annotation, you need strong attention to detail, accuracy, and basic data handling skills, often supported by a high school diploma or equivalent. Familiarity with annotation platforms, data labeling software, or content management systems is frequently required, though specific certifications are rare. Excellent communication, time management, and the ability to focus on repetitive tasks distinguish top performers in this role. These skills are crucial because accurate and consistent data annotation directly impacts the quality of machine learning models and AI applications.

What does a data annotator do?

A data annotator labels and tags data such as images, text, or videos to help machine learning models understand and learn from the data. They use tools and follow guidelines to ensure accuracy and consistency, often working with large datasets in a structured environment. Attention to detail and knowledge of annotation tools are important for this role.

Do people actually make money on data annotation?

Data annotation jobs can provide a source of income, with pay rates varying based on the complexity of tasks, platform, and experience. Many annotators earn hourly or per-task wages, but earnings often depend on the volume of work completed and the employer's pay structure.

Is it hard to get hired for data annotation?

Getting hired for a data annotation role generally depends on the employer's requirements, such as attention to detail and basic computer skills. Many positions are entry-level and may not require prior experience or certifications, making them accessible to a wide range of applicants. However, competition can vary based on the number of available jobs and the quality of applicants.
What are the most commonly searched types of Data Annotation jobs in Georgia? The most popular types of Data Annotation jobs in Georgia are:
What are popular job titles related to Data Annotation jobs in Georgia? For Data Annotation jobs in Georgia, the most frequently searched job titles are:
What job categories do people searching Data Annotation jobs in Georgia look for? The top searched job categories for Data Annotation jobs in Georgia are:
What cities in Georgia are hiring for Data Annotation jobs? Cities in Georgia with the most Data Annotation job openings:
Data Annotation jobs near you
Infographic showing various Data Annotation job openings in Georgia as of June 2026, with employment types broken down into 3% As Needed, 59% Full Time, 32% Part Time, and 6% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $40,839 per year, or $19.6 per hour.
Lead Data Scientist

Lead Data Scientist

Smarsh

Atlanta, GA • On-site

Full-time

Posted 13 days ago

Key responsibilities

  • Collect, analyze, and interpret small and large datasets to uncover meaningful insights to support the development of statistical methods and machine learning algorithms.

  • Lead the design, training, and deployment of NLP and transformer-based models for financial surveillance and supervisory use cases.

  • Contribute to model governance, documentation, and explainability frameworks aligned with internal and regulatory AI standards.

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.
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