Data Scientist

Koniag Government Services

Data Scientist building AWS machine learning, NLP, and predictive analytics solutions for government IT call centers. Improving forecasting, SLA performance, staffing, and customer experience through operational data.

Posted 8/5/2026full-timeRemote • Tennessee • 🇺🇸 United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and deploying machine learning models and advanced analytical solutions, with a strong focus on NLP, predictive analytics, and MLOps practices. Proficient in utilizing AWS AI/ML services and managing complex data science initiatives to drive operational improvements.

Highest-signal resume keywords

Machine Learning Model DevelopmentNatural Language Processing (NLP)AWS AI/ML ServicesMLOps PracticesPython Proficiency

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data ScienceMachine LearningPredictive AnalyticsStatistical AnalysisData AcquisitionFeature EngineeringModel EvaluationData VisualizationText AnalyticsData Cleaning

Soft Skills

Written CommunicationOral CommunicationMentorshipProject Management

Tools & Technologies

AWS SageMakerPythonNumPyPandasScikit-learnTensorFlowPyTorchKerasPower BITableau

Certifications & Qualifications

AWS Certifications

Industry Keywords

IT Call CenterFederal SecurityAI GovernanceFedRAMPGovernment Security Clearance

Tech Stack

Tools & technologies

AWSCloudDockerKerasKubernetesNoSQLNumpyPandasPythonPyTorchScikit-LearnSQLTableauTensorflow

About the role

Key responsibilities & impact

Lead end-to-end development, implementation, and refinement of data science and machine learning solutions for IT Call Center operational challenges.
Identify and prioritize high-value data science use cases with program leadership, data analysts, AWS AI Practitioner, and government stakeholders.
Design data acquisition, cleaning, transformation, and feature engineering pipelines for multi-source call center data.
Develop, train, validate, and deploy supervised, unsupervised, and reinforcement learning models using ML frameworks and AWS AI/ML services.
Design NLP and text analytics solutions for call transcripts, ticket notes, chat logs, and customer feedback.
Develop predictive models for call volumes, staffing requirements, SLA risks, and proactive operational decisions.
Design scalable, cloud-native data science architectures on AWS.
Develop MLOps pipelines including model versioning, automated retraining, CI/CD, performance monitoring, and drift detection.
Create data visualizations, analytical reports, and executive briefings for non-technical stakeholders.
Integrate data science outputs into operational reports, dashboards, and decision-support tools.
Conduct model assessments, A/B testing, and experimental design analyses.
Ensure compliance with federal security, privacy, responsible AI, AWS GovCloud, and FedRAMP requirements.
Provide technical guidance and mentorship to analysts and junior technical team members.
Maintain technical documentation, deployment procedures, and monitoring runbooks.
Track emerging data science and AWS AI/ML technologies and identify improvement opportunities.
Support business development and proposal activities as needed.

Requirements

What you’ll need

Master's degree in Data Science, Statistics, Mathematics, Computer Science, Machine Learning, or related quantitative field required; relevant experience may substitute for an advanced degree.
4+ years of hands-on data science experience.
Experience designing, developing, and deploying machine learning models and advanced analytical solutions.
Experience developing and deploying NLP, predictive analytics, and machine learning solutions using Python and industry-leading ML frameworks.
Experience with AWS AI/ML services, including Amazon SageMaker, Amazon Comprehend, Amazon Transcribe, or equivalent cloud-based ML platforms.
Experience working with large, complex, multi-source datasets.
Exceptional written and oral English communication skills.
Expert-level Python proficiency, including NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, Keras, NLTK, SpaCy, and Matplotlib.
Deep expertise in supervised, unsupervised, and reinforcement learning; ensemble methods; neural networks; deep learning; and model evaluation/validation.
Advanced NLP and text analytics proficiency, including tokenization, named entity recognition, sentiment analysis, topic modeling, text classification, and transformer model fine-tuning/deployment.
Demonstrated Amazon SageMaker proficiency for end-to-end ML pipelines, deployment, monitoring, and retraining.
Strong SQL and NoSQL proficiency.
Experience designing and implementing MLOps practices, including model versioning, ML CI/CD, automated retraining, and drift detection.
Advanced proficiency with Power BI, Tableau, Matplotlib, Seaborn, or Plotly.
Strong statistical analysis knowledge, including hypothesis testing, regression, time series analysis, Bayesian inference, and experimental design.
Ability to manage multiple complex initiatives independently and meet established timelines.
Ability to obtain and maintain a government security clearance as required.
Preferred/desired: doctoral degree, federal contracting or AWS GovCloud experience, IT call center/service desk experience, AWS certifications, federal security and AI governance familiarity, Bedrock/LLM/generative AI, data engineering, graph analytics, anomaly detection, forecasting, RLHF, causal inference, Docker, Kubernetes, and proposal-writing experience.

Benefits

Comp & perks

Health, dental and vision insurance
401K with company matching
Flexible spending accounts
Paid holidays
Three weeks paid time off
Competitive compensation