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Remote Data Science Jobs in Springfield, TN (NOW HIRING)

Koniag, Inc.

Data Scientist (Remote)

Nashville, TN · On-site +1

This position is Remote. We offer competitive compensation and an extraordinary benefits package ... Ensure all data science solution development and deployment activities comply with applicable ...

kgs

Data Scientist (Remote)

Nashville, TN · Remote

This position is Remote. We offer competitive compensation and an extraordinary benefits package ... Ensure all data science solution development and deployment activities comply with applicable ...

kgs

Data Scientist (Remote)

Nashville, TN · Remote

This position is Remote. We offer competitive compensation and an extraordinary benefits package ... Ensure all data science solution development and deployment activities comply with applicable ...

Zortech Solutions

GenAI Architect

Nashville, TN · Remote

Job Title GenAI Architect Duration: 6+ Months Location Nashville, TN Remote Work 100% Primary Skills Data Science Required Skills * 12 and 15 years of experience with Data Science AI ML and ...

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All JobsData Science JobsRemote Data Science JobsRemote Data Science Jobs in Springfield, TN

Remote Data Science information

What are the key skills and qualifications needed to thrive as a remote data scientist, and why are they important?

To thrive as a Remote Data Scientist, you need strong analytical skills, proficiency in statistics, and a solid background in mathematics or computer science, often supported by a relevant degree. Expertise in programming languages such as Python or R, familiarity with machine learning libraries, and experience with cloud-based data platforms are typically required. Excellent communication, self-motivation, and time management skills help you effectively collaborate and deliver results in a remote environment. These skills ensure accurate data analysis, meaningful insights, and successful teamwork despite physical distance.

How do remote data scientists typically collaborate with cross-functional teams to deliver insights?

Remote data scientists often work closely with product managers, engineers, and business analysts using digital collaboration tools such as Slack, Zoom, and project management platforms. Regular virtual meetings, code sharing via Git repositories, and clear documentation are essential to ensure alignment and transparency. While working remotely can present challenges in communication, proactive updates and scheduled syncs help foster strong teamwork and keep projects on track.

What is remote data science?

Remote data science refers to the practice of performing data analysis, modeling, and interpretation tasks from a location outside of a traditional office, such as from home or a co-working space. Remote data scientists use tools like Python, R, and SQL to analyze data, build predictive models, and communicate insights to stakeholders, all while collaborating virtually with their teams. This setup offers flexibility and can increase access to global job opportunities, but also requires strong self-motivation and communication skills to be effective.

What are the qualifications to get a remote data science job?

The qualifications for a remote data scientist depend in large part on your employer and their industry. Most employers expect remote data science professionals to have at least a bachelor’s degree in statistics, math, computer science, or a related field. Some expect postgraduate degrees in a field like data mining or machine learning or demonstrable skills in these areas. As a remote worker, you need access to relevant programs and an internet connection. You may also want to pursue certification, such as becoming a Certified Analytics Professional (CAP).

Can I work remotely as a remote data scientist?

Yes, many remote data scientist positions are available, allowing professionals to work from anywhere with a reliable internet connection. These roles often require skills in programming, data analysis, and familiarity with tools like Python, R, or SQL, and may involve collaboration through online platforms. Remote work arrangements are common in the data science field, especially with the increasing adoption of cloud-based tools and flexible schedules.

What is the difference between Remote Data Science vs Remote Data Analyst?

AspectRemote Data ScienceRemote Data Analyst
Required CredentialsDegree in Data Science, Statistics, or related field; programming skills in Python/R; knowledge of machine learningDegree in Statistics, Mathematics, or related field; proficiency in Excel, SQL, and data visualization tools
Work EnvironmentCollaborative teams, research-focused, often involves building models and algorithmsData reporting, visualization, and interpreting data trends for decision-making
Employer & Industry UsageTech companies, finance, healthcare, e-commerceMarketing agencies, retail, finance, healthcare

Remote Data Science involves developing predictive models and advanced analytics, requiring programming and machine learning skills. Remote Data Analysts focus on interpreting data, creating reports, and visualizations. While both roles analyze data remotely, Data Scientists typically handle more complex modeling tasks, whereas Data Analysts focus on data interpretation and reporting.

More about Remote Data Science jobs
What cities near Springfield, TN are hiring for Remote Data Science jobs? Cities near Springfield, TN with the most Remote Data Science job openings:
Remote Data Science jobs near you
Infographic showing various Remote Data Science job openings in Springfield, TN as of August 2026, with employment types broken down into 1% As Needed, 81% Full Time, 14% Part Time, and 4% Contract. Highlights an 85% Physical, 4% Hybrid, and 11% Remote job distribution.

Data Scientist (Remote)

Koniag, Inc.

Nashville, TN • On-site, Remote

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted 9 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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