Data Scientist

This is a fully remote position, open to applicants in Tennessee.

📋 Description

• Oversee the comprehensive development, implementation, and enhancement of data science and machine learning solutions tailored for IT Call Center operations.

• Create predictive models for call volume forecasting, SLA risk assessment, agent performance analysis, and customer satisfaction evaluation.

• Collaborate with program leadership, data analysts, AWS AI Practitioners, and government stakeholders to identify, prioritize, and define high-impact data science use cases.

• Architect data acquisition, cleansing, transformation, and feature engineering workflows.

• Develop, train, validate, and deploy models across supervised, unsupervised, and reinforcement learning paradigms.

• Design NLP and text analytics frameworks for processing call transcripts, ticket notes, chat logs, and customer feedback.

• Construct predictive models addressing call volumes, staffing needs, SLA risks, and facilitating proactive decision-making.

• Create scalable, cloud-native data science infrastructures on AWS utilizing SageMaker, Bedrock, Lambda, Kinesis, Glue, and associated services.

• Establish MLOps pipelines featuring model versioning, automated retraining, CI/CD, monitoring, and drift detection.

• Generate data visualizations, analytical reports, and executive summaries targeted at non-technical stakeholders.

• Integrate data science outputs into operational reporting, dashboards, and decision-support systems.

• Execute model performance evaluations, A/B testing, and experimental design assessments.

• Ensure adherence to federal security, privacy, responsible AI principles, AWS GovCloud, and FedRAMP standards.

• Offer technical mentorship and guidance to data analysts and junior technical team members.

• Maintain comprehensive technical documentation, deployment protocols, and operational monitoring playbooks.

• Stay abreast of advancements in data science, machine learning, and AWS AI/ML technologies.

• Assist in business development and proposal initiatives.

⛳️ Requirements

• Master's degree in Data Science, Statistics, Mathematics, Computer Science, Machine Learning, or a closely related quantitative discipline from an accredited institution; relevant experience may substitute for an advanced degree.

• 4+ years of practical experience in a data science capacity.

• Proven experience in designing, developing, and deploying machine learning models and advanced analytical solutions.

• Familiarity with developing and deploying NLP, predictive analytics, and machine learning solutions using Python and leading industry ML frameworks.

• Experience utilizing AWS AI and ML services, such as Amazon SageMaker, Amazon Comprehend, Amazon Transcribe, or comparable cloud-based ML platform services.

• Proficiency in managing large, intricate, multi-source datasets.

• Outstanding written and verbal communication skills in English.

• Expert knowledge of Python and data science libraries including NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, Keras, NLTK, SpaCy, and Matplotlib.

• Comprehensive understanding of supervised learning, unsupervised learning, reinforcement learning, ensemble methods, neural networks, deep learning architectures, and model evaluation and validation techniques.

• Advanced capabilities in NLP and text analytics, covering tokenization, named entity recognition, sentiment analysis, topic modeling, text classification, and transformer-based language model fine-tuning and deployment.

• Demonstrated expertise in utilizing Amazon SageMaker for end-to-end ML pipeline construction.

• Strong command of SQL and NoSQL databases.

• Experience in designing and executing MLOps practices and pipelines, including model versioning, CI/CD, automated retraining, and drift detection.

• Advanced skills in Power BI, Tableau, Matplotlib, Seaborn, or Plotly.

• Solid foundation in hypothesis testing, regression analysis, time series analysis, Bayesian inference, and experimental design.

• Capability to obtain and maintain a government security clearance as necessary.

• Ability to operate independently and manage multiple complex projects in a remote setting.

🏝️ Benefits

• Medical, dental, and vision insurance.

• 401(k) retirement plan.

• Paid time off.

• Paid parental leave.

• Life and disability insurance.

• Flexible spending accounts.

• Commuter benefits.

• Tuition reimbursement.

• Fully remote work environment.