Data Scientist (Remote)

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, an