Data Scientist
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Job Description
Data Scientist developing AWS machine learning, NLP, and predictive analytics solutions for Koniag’s government IT call center. Improving forecasting, SLA performance, and customer experience.
Responsibilities:
- Lead end-to-end development, implementation, and refinement of data science and machine learning solutions for IT Call Center operations
- Develop predictive call volume forecasting, SLA risk prediction, agent performance modeling, and customer satisfaction analytics
- Identify, prioritize, and scope 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
- Develop, train, validate, and deploy supervised, unsupervised, and reinforcement learning models
- Design NLP and text analytics solutions for call transcripts, ticket notes, chat logs, and customer feedback
- Build predictive models for call volumes, staffing requirements, SLA risks, and proactive decision making
- Design scalable cloud-native data science architectures on AWS using SageMaker, Bedrock, Lambda, Kinesis, Glue, and related services
- Develop MLOps pipelines with model versioning, automated retraining, CI/CD, monitoring, and drift detection
- Create data visualizations, analytical reports, and executive briefings for non-technical stakeholders
- Integrate data science outputs into operational reporting, dashboards, and decision-support tools
- Conduct model performance 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 data analysts and junior technical team members
- Maintain technical documentation, deployment procedures, and operational monitoring runbooks
- Stay current on data science, machine learning, and AWS AI/ML developments
- Support business development and proposal efforts
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
- 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 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
- Exceptional written and oral communication skills in English
- Expert-level proficiency in Python and data science libraries including NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, Keras, NLTK, SpaCy, and Matplotlib
- Deep expertise in supervised learning, unsupervised learning, reinforcement learning, ensemble methods, neural networks, deep learning architectures, and model evaluation and validation
- Advanced proficiency in NLP and text analytics, 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
- Strong proficiency in SQL and NoSQL
- Experience designing and implementing MLOps practices and pipelines, including model versioning, CI/CD, automated retraining, and drift detection
- Advanced proficiency in Power BI, Tableau, Matplotlib, Seaborn, or Plotly
- Strong understanding of hypothesis testing, regression analysis, time series analysis, Bayesian inference, and experimental design
- Ability to obtain and maintain a government security clearance as required
- Ability to work independently and manage multiple complex initiatives in a remote environment
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
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