Data Scientist (Remote)

Koniag Government Services•Nashville, TN

7d•Remote

About The Position

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.

Requirements

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.
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.

Nice To Haves

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.
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.

Responsibilities

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.