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Remote Amazon Data Annotation Jobs in Tennessee (NOW HIRING)

Koniag, Inc.

Data Scientist (Remote)

Nashville, TN · On-site +1

Demonstrated proficiency in Amazon SageMaker for end-to-end ML pipeline development, including data preparation, model training, hyperparameter tuning, model deployment, and automated model ...

kgs

Data Scientist (Remote)

Nashville, TN · Remote

Demonstrated proficiency in Amazon SageMaker for end-to-end ML pipeline development, including data preparation, model training, hyperparameter tuning, model deployment, and automated model ...

kgs

Data Scientist (Remote)

Nashville, TN · Remote

Demonstrated proficiency in Amazon SageMaker for end-to-end ML pipeline development, including data preparation, model training, hyperparameter tuning, model deployment, and automated model ...

micro1 AI

AI Trainer - Microbiology Expert

Memphis, TN · Remote

$70 - $90/hr

Remote micro1 is engaging Microbiologists to contribute their scientific expertise to a unique ... Document experimental findings and processes with a focus on clarity for AI training data.

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Remote Amazon Data Annotation information

What is the difference between Remote Amazon Data Annotation vs Remote Mechanical Turk Worker?

AspectRemote Amazon Data AnnotationRemote Mechanical Turk Worker
CredentialsNo formal certifications required, but attention to detail helpsNo formal certifications required, basic task understanding needed
Work EnvironmentRemote, flexible hours, online platformRemote, flexible hours, online micro-task platform
Employer & IndustryAmazon, e-commerce, AI training dataVarious clients, data labeling, surveys, research

Remote Amazon Data Annotation involves labeling data specifically for Amazon's AI and e-commerce needs, often requiring attention to detail. Mechanical Turk workers perform a variety of micro-tasks across industries. While both are remote and flexible, data annotation is more specialized for AI training, whereas Mechanical Turk offers broader task types.

What are common challenges faced by remote Amazon data annotation specialists and how can they be addressed?

Remote Amazon Data Annotation specialists often encounter challenges such as maintaining consistency and accuracy across large volumes of data, managing repetitive tasks, and staying engaged while working independently. To address these, it's important to develop a strong attention to detail, utilize quality control tools provided by the platform, and take regular breaks to minimize fatigue. Additionally, staying connected with your team through regular check-ins and feedback sessions can help ensure alignment on annotation guidelines and improve overall performance.

What is a remote Amazon data annotation job?

Remote Amazon Data Annotation jobs involve labeling, categorizing, or tagging data such as images, text, or audio to help train machine learning models used by Amazon. Employees work from home using specialized tools to ensure accuracy and consistency in the data provided. These roles often require attention to detail, the ability to follow guidelines, and sometimes specific domain knowledge depending on the project. Data annotation is essential for improving the performance of AI systems in tasks like product recommendations, voice recognition, and search algorithms. These roles may be full-time, part-time, or project-based, offering flexibility for remote workers.

What skills and qualifications are needed for a remote Amazon data annotation specialist?

To thrive as a Remote Amazon Data Annotation Specialist, you need strong attention to detail, analytical thinking, and familiarity with data labeling concepts, often supported by a high school diploma or relevant experience. Competence with web-based annotation tools, cloud-based platforms, and sometimes Amazon-specific data systems is typically required. Diligence, consistency, effective communication, and the ability to work independently are valuable soft skills in this role. These skills and qualities are important to ensure high-quality, accurate data labeling that supports effective machine learning and AI model development.

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

The most popular types of Amazon Data Annotation jobs in Tennessee are:

What are popular job titles related to Remote Amazon Data Annotation jobs in Tennessee?

For Remote Amazon Data Annotation jobs in Tennessee, the most frequently searched job titles are:

What job categories do people searching Remote Amazon Data Annotation jobs in Tennessee look for?

The top searched job categories for Remote Amazon Data Annotation jobs in Tennessee are:

What cities in Tennessee are hiring for Remote Amazon Data Annotation jobs?

Cities in Tennessee with the most Remote Amazon Data Annotation job openings:

Remote Amazon Data Annotation jobs near you
Infographic showing various Remote Amazon Data Annotation job openings in Tennessee as of August 2026, with employment types broken down into 1% As Needed, 82% Full Time, 13% Part Time, and 4% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution.

Data Scientist (Remote)

Koniag, Inc.

Nashville, TN • On-site, Remote

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted 12 days ago

Job description

Koniag Services Inc., a Koniag Government Services company, is seeking a talented and innovative Data Scientist to support the development and implementation of advanced data science, machine learning, and predictive analytics solutions for our IT Call Center serving government clients. The ideal candidate is a highly analytical and technically sophisticated professional with deep expertise in data science methodologies, machine learning model development, and statistical analysis, combined with a strong understanding of IT call center operations and service delivery environments. They bring a passion for transforming complex, large-scale operational data into actionable intelligence, a collaborative and solutions-oriented work ethic, and the ability to work independently and effectively in a fully remote environment to deliver data science solutions that drive measurable improvements in IT Call Center performance, efficiency, and customer experience. Ability to obtain a government security clearance may be required to support Koniag Services Inc. and our government customers. This position is Remote.
We offer competitive compensation and an extraordinary benefits package including health, dental and vision insurance, 401K with company matching, flexible spending accounts, paid holidays, three weeks paid time off, and more.
Position Description:
The Data Scientist will be responsible for the design, development, implementation, and ongoing refinement of advanced data science and machine learning solutions that support IT Call Center operational performance, predictive intelligence, and continuous improvement objectives. This individual will work closely with program leadership, data analysts, the AWS AI Practitioner, the AWS Solutions Architect, and government stakeholders to identify high-value data science opportunities, develop and deploy analytical models, and translate complex data science outputs into clear, actionable insights that inform strategic and operational decision making. Principal responsibilities will include but are not limited to:
  • Lead the end-to-end development, implementation, and ongoing refinement of advanced data science and machine learning solutions that address high-priority IT Call Center operational challenges and performance improvement opportunities, including predictive call volume forecasting, SLA risk prediction, agent performance modeling, and customer satisfaction analytics.
  • Collaborate with program leadership, data analysts, and the AWS AI Practitioner to identify, prioritize, and scope data science use cases that deliver the highest operational value for the IT Call Center program and government customer.
  • Design and execute comprehensive data acquisition, cleaning, transformation, and feature engineering pipelines that prepare raw call center operational data from multiple sources for advanced analytical and machine learning modeling purposes.
  • Develop, train, validate, and deploy supervised, unsupervised, and reinforcement learning models using industry-leading machine learning frameworks and AWS AI and ML platform services, ensuring all models meet defined performance, accuracy, and reliability standards prior to operational deployment.
  • Design and implement natural language processing (NLP) and text analytics solutions that analyze call transcripts, ticket notes, chat logs, and customer feedback data to identify sentiment trends, topic clusters, emerging issues, and customer experience improvement opportunities.
  • Develop and maintain predictive analytics models that leverage historical and real-time call center operational data to forecast call volumes, predict staffing requirements, identify at-risk SLA performance periods, and support proactive operational decision making.
  • Partner with the AWS AI Practitioner and AWS Solutions Architect to design and implement scalable, cloud-native data science solution architectures on AWS, leveraging Amazon SageMaker, Amazon Bedrock, AWS Lambda, Amazon Kinesis, AWS Glue, and related AWS data and AI services.
  • Develop and maintain robust MLOps pipelines and practices including model versioning, automated retraining workflows, continuous integration and delivery for ML models, and comprehensive model performance monitoring and drift detection frameworks.
  • Design and develop advanced data visualizations, analytical reports, and executive-level briefing materials that communicate complex data science findings and model outputs clearly and compellingly to non-technical program leadership and government stakeholders.
  • Collaborate with data analysts to ensure data science outputs are integrated effectively into operational reporting frameworks, performance dashboards, and decision support tools used by program leadership and call center operations staff.
  • Conduct rigorous model performance assessments, A/B testing, and experimental design analyses to evaluate the operational impact of deployed data science solutions and inform continuous model improvement activities.
  • Ensure all data science solution development and deployment activities comply with applicable federal security requirements, data privacy regulations, responsible AI governance standards, AWS GovCloud policies, and FedRAMP authorization requirements.
  • Provide technical guidance, mentorship, and subject matter expertise to data analysts and junior technical team members on data science methodologies, machine learning concepts, statistical analysis techniques, and AWS AI and ML service capabilities.
  • Develop and maintain comprehensive technical documentation for all data science solutions including model design documents, feature engineering specifications, training and validation results, deployment procedures, and operational monitoring runbooks.
  • Stay current on emerging data science methodologies, machine learning research, AWS AI and ML platform updates, and industry best practices, proactively identifying opportunities to leverage new techniques and technologies to enhance IT Call Center operational intelligence and performance.
  • Support business development activities as needed, including contributing to proposal efforts with data science capability narratives, technical solution concepts, analytical methodology descriptions, and relevant past performance documentation.

Education and Experience:
Required:
  • Master's degree in Data Science, Statistics, Mathematics, Computer Science, Machine Learning, or a related quantitative field from an accredited college or university. Relevant experience may be considered in lieu of an advanced degree.
  • 4+ years of hands-on experience in a data science role, with demonstrated experience designing, developing, and deploying machine learning models and advanced analytical solutions in a structured operational environment.
  • Demonstrated experience developing and deploying NLP, predictive analytics, and machine learning solutions using Python and industry-leading ML frameworks.
  • Experience leveraging AWS AI and ML services including Amazon SageMaker, Amazon Comprehend, Amazon Transcribe, or equivalent cloud-based ML platform services.
  • Experience working with large, complex, multi-source datasets in a structured analytical environment.

Preferred:
  • Doctoral degree (Ph.D.) in Data Science, Statistics, Mathematics, Computer Science, or a related quantitative field.
  • Prior experience applying data science methodologies within a federal government contracting or AWS GovCloud environment.
  • Experience supporting data science solution development for an IT call center, service desk, or IT managed services program.
  • AWS Certified Machine Learning - Specialty certification or AWS Certified AI Practitioner certification.
  • Experience working in a fully remote data science role within a government contracting environment.

Required Skills and Competencies:
  • Exceptional communication skills in English - both written and oral - with the ability to explain complex data science concepts, model outputs, and analytical findings clearly and compellingly to non-technical program leadership, government stakeholders, and cross-functional team members in a remote work environment.
  • Expert-level proficiency in Python for data science and machine learning development, including extensive experience with core data science libraries such as NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, Keras, NLTK, SpaCy, and Matplotlib.
  • Deep expertise in machine learning theory and practice, including supervised learning, unsupervised learning, reinforcement learning, ensemble methods, neural networks, deep learning architectures, and model evaluation and validation methodologies.
  • Advanced proficiency in natural language processing (NLP) and text analytics techniques, including tokenization, named entity recognition, sentiment analysis, topic modeling, text classification, and transformer-based language model fine-tuning and deployment.
  • Demonstrated proficiency in Amazon SageMaker for end-to-end ML pipeline development, including data preparation, model training, hyperparameter tuning, model deployment, and automated model monitoring and retraining.
  • Strong proficiency in SQL and NoSQL data querying languages for extracting, transforming, and analyzing large-scale datasets from relational databases, data warehouses, and ITSM platforms.
  • Demonstrated experience designing and implementing MLOps practices and pipelines, including model versioning, CI/CD for ML, automated retraining workflows, and model performance drift detection and alerting.
  • Advanced proficiency in data visualization tools and libraries including Microsoft Power BI, Tableau, Matplotlib, Seaborn, or Plotly for communicating complex data science findings and model outputs to technical and non-technical audiences.
  • Strong understanding of statistical analysis concepts and methods including hypothesis testing, regression analysis, time series analysis, Bayesian inference, and experimental design as applied to operational performance data.
  • Demonstrated ability to manage multiple complex data science initiatives simultaneously, work independently with minimal supervision, and deliver high-quality analytical outputs within established timelines in a remote work environment.
  • Ability to obtain and maintain a government security clearance as required.

Desired Skills and Competencies:
  • Active government security clearance (Secret or higher).
  • AWS Certified Machine Learning - Specialty certification.
  • AWS Certified AI Practitioner certification.
  • AWS Certified Solutions Architect - Associate certification demonstrating cloud architecture knowledge relevant to data science solution deployment.
  • Experience designing and implementing data science solutions within an AWS GovCloud environment in support of federal government FedRAMP authorization and data privacy requirements.
  • Familiarity with federal IT security frameworks and compliance requirements such as NIST SP 800-53, FedRAMP, FISMA, and emerging federal AI governance frameworks as they relate to data science solution design and deployment.
  • Experience with Amazon Bedrock, large language models (LLMs), and generative AI application development for intelligent automation and conversational AI use cases within a government IT service delivery context.
  • Familiarity with responsible AI principles, AI ethics frameworks, explainable AI (XAI) methodologies, and algorithmic bias detection and mitigation strategies as they apply to data science solution development in a federal government environment.
  • Experience with data engineering tools and platforms including AWS Glue, Amazon Kinesis, Amazon Redshift, Apache Spark, or equivalent big data processing and pipeline management technologies.
  • Experience with graph analytics, anomaly detection, or forecasting methodologies as applied to IT service delivery operational data.
  • Familiarity with reinforcement learning from human feedback (RLHF) and fine-tuning techniques for large language model customization in a government IT context.
  • Experience with A/B testing frameworks, causal inference methodologies, and experimental design for evaluating the operational impact of deployed data science solutions.
  • Knowledge of containerization and orchestration technologies such as Docker and Kubernetes as they relate to scalable ML model deployment and management in a cloud-native environment.
  • Experience contributing to business development efforts including proposal writing, data science capability development, analytical methodology narratives, and past performance documentation.

Our Equal Employment Opportunity Policy:
The company is an equal opportunity employer. The company shall not discriminate against any employee or applicant because of race, color, religion, creed, ethnicity, sex, sexual orientation, gender or gender identity (except where gender is a bona fide occupational qualification), national origin or ancestry, age, disability, citizenship, military/veteran status, marital status, genetic information or any other characteristic protected by applicable federal, state, or local law. We are committed to equal employment opportunity in all decisions related to employment, promotion, wages, benefits, and all other privileges, terms, and conditions of employment.
The company is dedicated to seeking all qualified applicants. If you require an accommodation to navigate or to apply to a position on our website, please contact Heaven Wood via e-mail at accommodations@koniag-gs.com or by calling 703-488-9377 to request accommodations.
Koniag Government Services (KGS) is an Alaska Native Owned corporation supporting the values and traditions of our native communities through an agile employee and corporate culture that delivers Enterprise Solutions, Professional Services and Operational Management to Federal Government Agencies. As a wholly owned subsidiary of Koniag, we apply our proven commercial solutions to a deep knowledge of Defense and Civilian missions to provide forward leaning technical, professional, and operational solutions. KGS enables successful mission outcomes for our customers through solution-oriented business partnerships and a commitment to exceptional service delivery. We ensure long-term su
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About Koniag

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Industry

Investment management and consulting services

Company size

501 - 1,000 Employees

Headquarters location

Kodiak, AK, US

Year founded

1972

Social media

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