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.
Editorial Process and Content Quality
This content is part of Lamwork's career intelligence platform and is developed using structured analysis of real-world job data, including publicly available job descriptions, skill requirements, and hiring patterns.
Lam Nguyen, Founder & Editorial Lead, defines the research framework behind Lamwork's career intelligence platform, including job role analysis, skills taxonomy, and structured career insights.
All content is reviewed by Thanh Huyen, Managing Editor, who oversees editorial quality, content consistency, and alignment with real-world role expectations and Lamwork's editorial standards.
Content is developed through a structured process that includes data analysis, role and skill mapping, standardized content formatting, editorial review, and periodic updates.
Content is reviewed and updated periodically to reflect changes in skills, role requirements, and labor market trends.
Learn more about our editorial standards.