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About This Role
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, 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.
Education and Experience:Required:
- 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.
Preferred:
- 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.
Required Skills and Competencies:
- 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.
Desired Skills and Competencies:
- 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.
Our Equal Employment Opportunity Policy:
The company is an equal opportunity employer. The company shall not discriminate against any employee or applicant because of race, color, religion, creed, ethnicity, sex, sexual orientation, gender or gender identity (except where gender is a bona fide occupational qualification), national origin or ancestry, age, disability, citizenship, military/veteran status, marital status, genetic information or any other characteristic protected by applicable federal, state, or local law. We are committed to equal employment opportunity in all decisions related to employment, promotion, wages, benefits, and all other privileges, terms, and conditions of employment.
The company is dedicated to seeking all qualified applicants. If you require an accommodation to navigate or to apply to a position on our website, please contact Heaven Wood via e\-mail at accommodations@koniag\-gs.com or by calling 703\-488\-9377 to request accommodations.
Koniag Government Services (KGS) is an Alaska Native Owned corporation supporting the values and traditions of our native communities through an agile employee and corporate culture that delivers Enterprise Solutions, Professional Services and Operational Management to Federal Government Agencies. As a wholly owned subsidiary of Koniag, we apply our proven commercial solutions to a deep knowledge of Defense and Civilian missions to provide forward leaning technical, professional, and operational solutions. KGS enables successful mission outcomes for our customers through solution\-oriented business partnerships and a commitment to exceptional service delivery. We ensure long\-term success with a continuous improvement approach while balancing the collective interests of our customers, employees, and native communities. For more information, please visit www.koniag\-gs.com.
Equal Opportunity Employer/Veterans/Disabled. Shareholder Preference in accordance with Public Law 88\-352
Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Koniag Government Services, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 789 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Koniag Government Services AI Hiring
Koniag Government Services has 2 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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