WHAT DOES A JUNIOR DATA SCIENTIST DO?
Published: Jan 10, 2026 - The Junior Data Scientist develops, tests, and transforms data while applying advanced analytical and machine-learning techniques to support forecasting, optimization, and pattern identification. This role involves gathering requirements, coordinating with stakeholders to validate analytical outputs, and creating visualizations and executive-level presentations that clearly communicate insights. The individual also supports productionized code, contributes to innovative analytics projects, and engages in continuous learning to advance the organization’s analytics capabilities.
A Review of Professional Skills and Functions for Junior Data Scientist
1. Junior Data Scientist Overview
- Project Leadership: Own and lead specific projects such as experiments or new capabilities that support operational delivery across the DfE.
- Business Analysis: Identify clear business needs and define the most suitable tools, data sources, and analytical techniques to address them.
- Stakeholder Engagement: Engage proactively with users and stakeholders to uncover root causes of problems and inefficiencies.
- Advisory Skills: Provide informed advice that goes beyond surface-level requirements gathering.
- Data Analysis: Analyze findings and translate results into clear, actionable insights.
- Stakeholder Communication: Communicate key findings effectively to diverse stakeholder groups.
- Data Visualization: Use interactive visualization techniques to present insights in a compelling and accessible way.
- Insight Dissemination: Disseminate data-driven insights to the education sector using modern visualization approaches.
- Capability Development: Actively contribute to the development of data science capability within the DfE.
- Knowledge Sharing: Share knowledge through code sharing, presentations, and show-and-tell sessions.
- Continuous Learning: Stay informed about emerging data science technologies and practices across government and industry.
2. Junior Data Scientist Functions
- Requirements Analysis: Interpret business requirements to develop logic based on the schema and available data.
- BI Development: Build dashboards and reports using SQL on a Business Intelligence platform.
- Self-Service Enablement: Support the adoption of BI user self-serve capabilities.
- Data Automation: Automate data movement from the AWS ecosystem to the BI platform using Airflow.
- Data Engineering: Build and maintain data pipelines from external data sources.
- Cross-Team Collaboration: Collaborate with members of the tech team, including data engineers, product operations managers, software engineers, and other data scientists, to align with project goals.
- ML Support: Support a senior data scientist in training and operationalizing machine learning models.
- ML Pipelines: Build and maintain machine learning pipelines.
3. Junior Data Scientist Accountabilities
- Research Development: Develop research studies, analyses, and projects that influence ACC CDCC programs and policies.
- Analytical Support: Provide analytical support to Mission Defense Teams, the Cyber Defense Operations Center, and related cyber mission assurance organizations.
- Hypothesis Testing: Conduct detailed analysis to develop and verify hypotheses, concepts, and technical proposals.
- Big Data Analytics: Apply big data platforms, algorithms, and analytical tools to address complex cybersecurity challenges.
- Quantitative Analysis: Use advanced quantitative methods and techniques to analyze large and complex datasets.
- Analytics Tooling: Create, develop, or apply existing tools and techniques to perform data analytics activities.
- Data Preparation: Gather, curate, and prepare diverse data sources for effective analysis.
- Insight Extraction: Extract meaningful insights from large-scale and heterogeneous datasets.
- Algorithm Development: Develop, test, and optimize algorithms and statistical models for threat intelligence discovery.
- Model Optimization: Reduce false positives while improving the accuracy and relevance of analytical outputs.
- Strategic Recommendations: Generate recommendations and courses of action for internal and external stakeholders.
- Executive Reporting: Provide data-driven analytical support to management at all organizational levels.
4. Junior Data Scientist Details
- Guided Delivery: Work under the supervision of a Lead Data Scientist to understand project requirements and apply best-practice methodologies.
- Requirements Translation: Support the translation of business and technical needs into structured analytical tasks.
- Data Management: Manage and manipulate large and complex datasets within a big data environment.
- Workflow Automation: Automate data processing workflows to improve efficiency and repeatability.
- Big Data Processing: Leverage technologies such as Hadoop, Spark, and Scala for scalable data processing.
- Programming Skills: Apply Python, R, and SQL to extract, transform, and analyze data.
- Solution Delivery: Deliver bespoke data science and machine learning solutions aligned with project objectives.
- Production Automation: Automate analytical and modeling workflows for production deployment.
- Model Development: Develop models using techniques such as gradient boosting, neural networks, and predictive modeling.
- Machine Learning: Apply both supervised and unsupervised learning methods, including regression and clustering approaches.
- Statistical Modeling: Use advanced statistical techniques such as Bayesian methods and principal component analysis to enhance model performance.
5. Junior Data Scientist Duties
- Data Assessment: Analyze raw data by assessing quality, completeness, and consistency.
- Data Preparation: Cleanse and structure data to ensure it is suitable for downstream processing.
- Dataset Engineering: Prepare datasets to support reliable and repeatable analytical workflows.
- Algorithm Design: Design prediction algorithms that are both accurate and scalable.
- Model Evaluation: Evaluate model performance to ensure robustness in production environments.
- Engineering Collaboration: Collaborate closely with engineering teams to transition analytical prototypes into production systems.
- Deployment Support: Support deployment by aligning analytical solutions with technical constraints.
- Insight Generation: Generate actionable insights that drive measurable business improvements.
6. Junior Data Scientist Details and Accountabilities
- Model Governance: Manage model governance requirements for business partners across ESI, EviCore, and external vendors.
- Regulatory Alignment: Ensure governance processes align with organizational standards and regulatory expectations.
- Model Review: Review model design, underlying assumptions, and performance against defined governance metrics.
- Risk Assessment: Assess model outputs to identify disparities and potential risks.
- Issue Mitigation: Provide clear guidance on mitigation actions to address identified governance issues.
- Cross-Functional Collaboration: Collaborate cross-functionally to enforce adherence to the model governance framework.
- Process Improvement: Support continuous enhancements to governance methodologies and processes.
- KPI Monitoring: Monitor and track model governance KPIs related to usage, performance, and disparity measures.
- Governance Reporting: Maintain oversight of governance reporting and performance monitoring activities.
- Governance Implementation: Participate in the ongoing implementation of governance procedures, including standardization and automation efforts.
- Model Validation: Conduct periodic model reviews and validation for models operating in production environments.
7. Junior Data Scientist Tasks
- Report Development: Design and develop reports and dashboards to support business decision-making.
- Dashboard Optimization: Maintain and optimize existing dashboards to ensure performance and accuracy.
- Data Governance: Ensure data governance standards are consistently applied across reporting assets.
- Compliance Support: Support compliance with data quality, security, and usage guidelines.
- Data Cataloging: Contribute to maintaining an up-to-date data catalog and reporting inventory.
- Documentation Management: Document data definitions, report logic, and ownership clearly.
- Tool Development: Build tools that support efficient data collection and informed decision-making.
- Decision Support: Develop decision support solutions tailored to stakeholder needs.
- Community Engagement: Actively participate in the Tableau Analyst Group to share knowledge and best practices.
8. Junior Data Scientist Roles
- Data Science Support: Provide data science support for the analysis of large and complex datasets.
- Advanced Analytics: Apply advanced analytical tools and computational techniques to uncover meaningful insights.
- Data Exploration: Explore diverse data sources to identify patterns and opportunities for discovery.
- Data Integration: Support the integration of structured and unstructured datasets into unified analytical frameworks.
- Data Alignment: Ensure data is prepared and aligned for effective downstream analysis.
- Data Flow Management: Monitor and manage data flows to support future analytical and modeling needs.
- Team Collaboration: Contribute as a team member supporting cross-functional groups with data-driven insights.
- Business Analysis: Assist in answering actionable business questions through quantitative analysis.
- Analytical Delivery: Support a range of data analysis and modeling initiatives across projects.
- Cross-Department Communication: Communicate clearly with other departments to ensure accuracy and consistency of information.
9. Junior Data Scientist Additional Details
- Algorithm Implementation: Implement algorithms for segmentation, predictive modeling, forecasting, simulation, and optimization.
- Model Selection: Select appropriate modeling techniques based on business objectives and data characteristics.
- Solution Development: Develop analytical solutions that are both effective and easy to manage.
- Machine Learning: Apply a range of machine learning approaches from simple to complex models.
- Problem Translation: Translate business problems into clear analytical frameworks.
- Data Modeling: Model data in ways that directly reflect business needs and priorities.
- Results Visualization: Visualize analytical results to support understanding and decision-making.
- Data Visualization: Design clear and actionable data visualizations for stakeholders.
- Analytics Advisory: Support businesses in selecting suitable analytics, reporting, and visualization solutions.
- Maturity Assessment: Assess current analytics maturity to identify strengths and limitations.
- Roadmap Planning: Define gaps and create roadmaps to industrialize and scale data streams.
10. Junior Data Scientist Essential Functions
- Model Maintenance: Maintain and continuously refine custom data models and algorithms applied to complex datasets.
- Model Development: Develop new data models and analytical methods to address evolving requirements.
- Production Readiness: Ensure models and algorithms are robust, efficient, and fit for production use.
- Test Documentation: Write and maintain comprehensive project test documentation.
- Quality Assurance: Support quality assurance through structured testing and validation processes.
- Software Development: Develop line-of-business software to support departmental research and development activities.
- Solution Design: Design software solutions that align with technical and operational needs.
- Experimental Planning: Plan and perform laboratory experiments to resolve complex development challenges.
- Experimental Validation: Apply systematic experimentation to validate technical assumptions and solutions.
- Prototyping: Create software and hardware prototypes to evaluate feasibility and performance.
- Infrastructure Support: Support the development and maintenance of technical infrastructure components.
- System Maintenance: Maintain and enhance hardware systems and system software to ensure operational reliability.
11. Junior Data Scientist Role Purpose
- Stakeholder Communication: Communicate clearly and effectively with a wide range of stakeholders.
- Audience Adaptation: Adapt communication style to suit technical and non-technical audiences.
- Business Acumen: Develop a strong understanding of the business context behind supported decisions.
- Insight Interpretation: Interpret analytical results to ensure they are meaningful and actionable.
- Adoption Enablement: Support high adoption of analytical outputs by aligning insights with business needs.
- Delivery Excellence: Ensure high-quality and on-time delivery in line with established standards and best practices.
- Analytical Modeling: Apply R or Python hands-on to design and develop analytical models.
- Data Mining: Mine data for insights that support strategic and operational decision-making.
- Analytics Culture: Promote a strong culture of modeling and analytics across the organization.
12. Junior Data Scientist General Responsibilities
- Data Science Solutions: Provide data science solutions for digital library development, human language translations, optical character recognition, and technical analysis of foreign language materials and technologies.
- Data Integration: Support data science solutions to integrate, ingest, reformat, and transform PAI data.
- Content Cataloging: Catalog and scan translated material to identify data of interest based on specific requests.
- Data Collection: Develop and maintain data collection solutions for PAI and OS information, including web scrapers and technologies for sensitive searching.
- NLP Collaboration: Work with a multi-disciplinary team to leverage data science and NLP tools to collect, organize, and analyze diverse science and technology information from multiple sources.
- Data Visualization: Use data visualization techniques to provide insights into large data sets and contribute to finished reports.
- Database Management: Build and maintain databases in support of analyst and subject matter expert use.
- NLP Application: Use NLP tools to support language translation and science and technology assessments.
- Independent Working: Work independently with some oversight and function effectively as part of a team in a joint working environment.
13. Junior Data Scientist Key Accountabilities
- Data Lifecycle Management: Develop, test, and transform data for the data science life cycle.
- Advanced Analytics: Apply advanced analytical techniques and data science methods, including forecasting, optimization, and machine learning, such as smoothing, queuing, forecasting patient demand, optimizing revenue, and identifying utilization patterns.
- Requirements Gathering: Collect requirements, perform literature reviews of techniques, and document analytical techniques and findings.
- Stakeholder Coordination: Coordinate with stakeholders across the organization to gain business understanding and validate advanced analytic outputs.
- Analytical Visualization: Design visualizations to support advanced analytical outputs in the business intelligence tool.
- Executive Reporting: Create executive-level presentations to synthesize findings and provide recommendations.
- Continuous Learning: Engage in continuous learning of advanced methods and best practices.
- Innovation Delivery: Work on innovative projects to advance the analytics roadmap.
- Code Maintenance: Support the maintenance and optimization of productionized code.