Technical Data & Automation Analyst Intern

PROJECT GUARDIAN TECHNICAL DATA & AUTOMATION ANALYST INTERN

ABOUT MORSBY, GORMAN, MCCARTHY LLC

Morsby, Gorman, McCarthy LLC is developing Project Guardian as a multidisciplinary, project-based initiative focused on infrastructure planning, implementation readiness, policy research, partnership development, evidence management, risk analysis, communications, and program coordination.

Project Guardian’s initial work centers on a proposed Kenya-focused infrastructure pilot. Participants will contribute to research, planning, analysis, documentation, and professional-quality project materials intended to help clarify the pilot’s feasibility, evidentiary foundation, funding requirements, implementation conditions, and potential pathways toward responsible replication or expansion.

Participation does not represent employment by, appointment to, or endorsement from any government, university, funder, development institution, community, or prospective partner. Project Guardian is an independent initiative of Morsby, Gorman, McCarthy LLC unless a separate written agreement states otherwise.

PROGRAM TERMS

• Program dates: October 19, 2026–January 29, 2027
• Holiday pause: December 19, 2026–January 3, 2027, during which no routine work is expected
• Fully remote and asynchronous-first
• Normally 5–10 hours per week
• Work exceeding 10 hours in a week requires advance written approval
• Participants should not ordinarily exceed approximately 15 hours in any week
• Unpaid educational internship
• Academic credit may be available only when approved by the participant’s institution
• No promise or expectation of future employment
• Reasonable academic and accessibility accommodations will be considered
• Participation is subject to applicable confidentiality, data-handling, authorship, attribution, acceptable-use, and release requirements

EDUCATIONAL STRUCTURE

This is a structured, project-based learning experience. Participants receive defined learning objectives, supervised assignments, written or documented feedback, revision opportunities, and exposure to data transformation, scripting, workflow automation, testing, documentation, infrastructure research, public policy, and multidisciplinary program work.

Assignments are designed to develop transferable technical and professional competencies through applied work of educational value. Participants may produce portfolio-appropriate materials, excerpts, or descriptions subject to confidentiality, intellectual-property, security, attribution, licensing, data-protection, and release requirements.

Inclusion of any project code, notebook, automation, repository, dataset, schema, screenshot, technical documentation, or deliverable in a portfolio requires prior written authorization. Authorized portfolio materials may require redaction, synthetic data, removal of credentials and internal details, modification, or explanatory limitations.

Academic credit may be available only when approved by the participant’s institution. Participants are responsible for initiating and satisfying their institution’s credit-approval process. Morsby, Gorman, McCarthy LLC will reasonably cooperate with legitimate institutional documentation, learning-agreement, supervision, evaluation, and verification requirements when provided with sufficient notice.

Academic credit is not guaranteed and is not required for participation. The availability, amount, classification, and conditions of credit are determined solely by the participant’s institution.

The internship is intended to complement academic study and professional development. Participants will not displace regular employees or independently perform production engineering, system administration, cybersecurity operations, or functions requiring licensed legal, privacy, security, engineering, financial, or other specialized professional judgment.

ROLE PURPOSE

The Technical Data & Automation Analyst supports Project Guardian by developing supervised technical methods for organizing, validating, transforming, reconciling, and processing approved information.

Assignments may involve scripts, formulas, notebooks, data-processing workflows, low-code automations, application-programming-interface prototypes, validation routines, reproducible analytical processes, and technical documentation.

The role emphasizes bounded prototypes, transparent logic, testing, documentation, human review, and safe handoff. It does not authorize unsupervised deployment to production systems.

This role is distinct from the Data & Systems Analyst. The Data & Systems Analyst primarily defines information requirements, structures, definitions, governance, quality needs, and system workflows. The Technical Data & Automation Analyst focuses on supervised implementation of bounded transformations, checks, prototypes, and repeatable technical processes based on approved requirements.

This is a hands-on contributor internship, not a production software-engineering, database-administration, system-administration, cybersecurity, or independent deployment position.

Participants will receive defined assignments, approved development environments or data, testing expectations, documented feedback, revision opportunities, and progressively more complex technical work where supported by readiness and educational value.

LEARNING OBJECTIVES

By the conclusion of the internship, the participant should be able to:

• Translate approved requirements into a bounded technical plan
• Distinguish exploration, prototype, test, staging, and production environments
• Develop readable and appropriately documented scripts, formulas, notebooks, or low-code workflows
• Transform and reconcile approved data while preserving source information and auditability
• Design validation rules and tests connected to documented requirements
• Use synthetic, public, or appropriately authorized data during development
• Handle configuration values, credentials, and secrets responsibly
• Explain technical logic, dependencies, assumptions, limitations, and failure modes
• Develop reproducible workflows that another authorized participant can review and rerun
• Apply version control, change documentation, testing, and peer-review practices
• Identify when automation creates privacy, security, licensing, bias, reliability, or operational risk
• Incorporate logging, exception handling, and human review into appropriate workflows
• Recognize when a task requires specialist cybersecurity, privacy, legal, engineering, or production-operations review
• Communicate technical findings and limitations to nontechnical collaborators
• Revise technical work in response to testing and documented feedback

PRIMARY RESPONSIBILITIES

Technical Scoping and Planning

• Translate approved data and workflow requirements into bounded implementation plans
• Clarify the intended user, input, output, environment, frequency, dependencies, and acceptance criteria
• Identify whether a task is exploratory, one-time, repeatable, prototype, or potentially production-relevant
• Define what the proposed technical work can and cannot establish
• Identify data-access, licensing, privacy, security, performance, maintenance, and support considerations
• Estimate complexity and identify questions that could materially change the approach
• Recommend an appropriately simple solution before introducing unnecessary automation
• Submit the plan for review before using sensitive information, external services, credentials, or production-connected resources
• Maintain traceability between the approved requirement and the proposed implementation

Data Preparation and Transformation

• Develop supervised methods for importing, parsing, validating, cleaning, normalizing, joining, reshaping, or exporting approved data
• Preserve original source information where required
• Document transformation logic, field mappings, exclusions, assumptions, and exceptions
• Distinguish deterministic transformations from judgment-based classifications or estimates
• Identify risks involving inconsistent identifiers, incompatible data grain, duplicates, missing values, invalid formats, and conflicting definitions
• Avoid silently changing or discarding source information
• Produce reconciliation summaries showing relevant input, output, exception, and exclusion counts
• Coordinate with the Data & Systems Analyst on approved definitions, schemas, quality rules, and mappings
• Coordinate with the Evidence and Quality Assurance Lead when transformations materially affect findings or claims

Validation and Quality Controls

• Translate approved data-quality requirements into validation checks
• Develop supervised tests for completeness, validity, consistency, uniqueness, expected ranges, formatting, and relationships
• Distinguish an unusual value from a confirmed error
• Identify false positives, false negatives, edge cases, and limitations in automated rules
• Preserve exception records for human review
• Avoid representing successful code execution as proof that the underlying data or conclusion is correct
• Document test inputs, expected results, actual results, and unresolved failures
• Recommend proportionate manual-review steps where automation cannot make a reliable determination
• Coordinate material quality concerns with the appropriate data, evidence, or workstream lead

Workflow Automation and Prototyping

• Develop supervised prototypes for repetitive, rules-based, and appropriately bounded workflows
• Document the manual process, proposed automation boundary, inputs, outputs, users, dependencies, and exceptions
• Identify where human review, approval, or judgment must remain in the process
• Compare expected benefits with setup, maintenance, error, access, and continuity costs
• Build prototypes using approved tools and environments
• Test normal cases, edge cases, invalid inputs, interrupted processes, and recovery behavior
• Avoid automating sensitive decisions, external communications, financial actions, access changes, or irreversible operations without specific authorization and safeguards
• Clearly label prototypes as nonproduction unless separately reviewed and approved
• Prepare handoff documentation explaining how the prototype works and what would be required before broader use

Scripting, Notebooks, and Reproducibility

• Develop readable scripts, notebooks, formulas, or low-code workflows for approved assignments
• Use meaningful names, modular logic, explanatory comments, and appropriate documentation
• Record software, library, package, and environment dependencies
• Separate configuration from reusable logic where appropriate
• Avoid embedding passwords, tokens, keys, or other secrets in code, notebooks, screenshots, or repositories
• Use version control or another approved change-record process when appropriate
• Include instructions for authorized users to run, test, and verify the work
• Record known limitations, unresolved issues, and expected failure conditions
• Ensure that outputs can be connected to their relevant inputs and processing steps

API and Integration Prototypes

• Review approved documentation for APIs or supported data-access methods
• Develop bounded prototypes using test, sandbox, synthetic, public, or otherwise authorized information
• Document endpoints, parameters, authentication requirements, rate limits, response formats, error conditions, and permitted use
• Validate returned information before relying on it
• Avoid using undocumented endpoints, bypassing controls, or exceeding authorized access
• Avoid creating integrations that expose confidential information or credentials
• Record dependencies on external providers, accounts, limits, and terms
• Coordinate with the Data & Systems Analyst on field mappings and integration requirements
• Treat all prototypes as nonproduction unless separately reviewed, secured, tested, and authorized

Testing and Technical Review

• Develop proportionate test plans connected to documented requirements and acceptance criteria
• Test expected cases, boundary conditions, missing inputs, invalid formats, duplicates, interruptions, and recovery behavior
• Record test results, unresolved defects, limitations, and retest status
• Participate in supervised peer review of technical logic and documentation
• Correct identified defects through an authorized revision process
• Avoid claiming completeness, security, scalability, or production readiness solely because a limited test passed
• Identify circumstances requiring performance, security, privacy, accessibility, or specialist review
• Preserve enough information for another authorized person to evaluate the test

Logging, Monitoring, and Exception Handling

• Develop proportionate logging and exception-handling approaches for approved prototypes
• Record relevant processing status, warnings, errors, and reconciliation information without unnecessarily exposing sensitive data
• Avoid placing passwords, credentials, personal information, confidential content, or restricted data in logs
• Distinguish recoverable exceptions from failures requiring human intervention
• Provide clear error messages and escalation instructions where appropriate
• Document how incomplete or failed processing should be identified
• Avoid representing prototype monitoring as certified production observability or security monitoring
• Coordinate unresolved operational risks with the appropriate project lead

Responsible Use of Artificial Intelligence and Automated Classification

• Use generative AI, machine-learning, or automated classification tools only when specifically permitted
• Document the tool, purpose, material inputs, review process, and known limitations when required
• Avoid entering confidential, personal, proprietary, licensed, or restricted information into unauthorized systems
• Verify automated summaries, classifications, citations, calculations, and extracted information against approved sources
• Evaluate potential hallucination, bias, inconsistency, provenance, and reproducibility concerns
• Maintain meaningful human review for material analytical or operational outputs
• Avoid using automated tools to make unsupervised eligibility, employment, disciplinary, legal, financial, safety, or stakeholder decisions
• Clearly identify synthetic, generated, estimated, or tool-assisted content when its status is material

Technical Documentation and Handoff

• Prepare readme files, setup instructions, process descriptions, data-flow diagrams, test notes, and troubleshooting guidance
• Document inputs, outputs, dependencies, configuration requirements, limitations, and approval status
• Explain technical logic in language accessible to relevant nontechnical collaborators
• Identify maintenance, continuity, access, and ownership requirements
• Maintain version and change records
• Avoid assuming that another person will possess undocumented knowledge
• Prepare orderly handoffs for authorized continuation, archival, or retirement of prototypes
• Coordinate storage and access with the Knowledge and Data-Room Manager

Cross-Functional Collaboration

• Support approved technical needs across research, data, operations, funding, risk, evidence, communications, visualization, and engineering workstreams
• Confirm the substantive purpose before implementing a technical solution
• Coordinate with data contributors on definitions, mappings, and quality expectations
• Coordinate with researchers concerning source meaning and limitations
• Coordinate with visualization contributors concerning output structure and refresh needs
• Communicate defects, limitations, delays, and technical risks promptly
• Escalate material conflicts in requirements, access, data use, or implementation approach
• Maintain usable handoff documentation when responsibility changes

SUPERVISION, FEEDBACK, AND REVISION

The Technical Data & Automation Analyst will receive assignments, guidance, and feedback from designated project or workstream leadership.

The analyst may coordinate closely with the Data & Systems Analyst, Evidence and Quality Assurance Lead, Knowledge and Data-Room Manager, Monitoring, Risk and Continuity Lead, and Deputy Program and Operations Manager.

The educational workflow is expected to include:

• Initial orientation to Project Guardian’s scope, technical environment, data-handling rules, and limitations
• Defined assignments connected to the role’s learning objectives
• Approved data, development environments, templates, examples, and requirements
• Scheduled or milestone-based supervisory review
• Code, notebook, formula, workflow, or documentation review where appropriate
• Written, annotated, or otherwise documented feedback
• Opportunities to ask questions and clarify expectations
• Reasonable opportunities to test and revise substantive work
• Progressively more complex assignments where supported by demonstrated readiness
• Periodic reflection on professional development and learning progress
• A final review or evaluation where appropriate

Feedback may address technical reasoning, readability, correctness, testing, documentation, reproducibility, data handling, error management, security awareness, proportionality, communication, collaboration, and the participant’s ability to incorporate revisions.

EXPECTED EDUCATIONAL OUTPUTS

Depending on project needs, participant readiness, available tools, approved data, and supervisory direction, outputs may include:

• Technical requirements and implementation notes
• Data-import, cleaning, validation, transformation, or reconciliation scripts
• Reproducible analytical notebooks
• Spreadsheet formulas or controlled workbook workflows
• Low-code automation prototypes
• API or integration prototypes using approved environments
• Validation-rule libraries and exception reports
• Test plans, test cases, and test-result records
• Input-to-output reconciliation summaries
• Data-processing and workflow diagrams
• Configuration and dependency documentation
• Readme files and user guidance
• Logging and exception-handling recommendations
• Responsible-AI evaluation notes
• Technical risk and limitation records
• Handoff, archival, or retirement documentation
• Substantive original technical, analytical, and written work

Outputs are educational and developmental materials. Their production does not guarantee that they will be adopted, deployed, externally distributed, certified, or used in a production environment.

PERFORMANCE AND LEARNING ASSESSMENT

Progress may be assessed according to:

• Correct translation of approved requirements into bounded technical work
• Readability, organization, and maintainability of scripts, notebooks, formulas, or workflows
• Accuracy and traceability of transformations
• Quality and completeness of validation and testing
• Appropriate treatment of edge cases, errors, missing values, and exceptions
• Transparency concerning assumptions, dependencies, limitations, and unresolved defects
• Responsible handling of data, credentials, access, licensing, and external services
• Reproducibility and quality of documentation
• Ability to explain technical work to nontechnical collaborators
• Appropriate use of human review and escalation
• Compliance with confidentiality, responsible-AI, attribution, and acceptable-use requirements
• Ability to receive, evaluate, and incorporate feedback
• Reliability in meeting agreed milestones or communicating constraints early
• Effectiveness in multidisciplinary collaboration
• Growth against the stated learning objectives

Assessment is intended to support learning and program administration. It is not a licensed professional examination or certification in software engineering, data engineering, systems administration, cybersecurity, artificial intelligence, privacy, or any other professional field.

PREFERRED QUALIFICATIONS

Candidates may be undergraduate or graduate students, postdoctoral researchers, recent graduates, alumni, or early-career professionals. Relevant classroom, laboratory, volunteer, internship, military, community, entrepreneurial, or professional experience may be considered. No single academic major is required.

Relevant academic areas may include computer science, data science, information systems, software engineering, statistics, business analytics, information science, applied mathematics, engineering, computational social science, or related interdisciplinary fields.

Strong candidates will demonstrate:

• Analytical problem-solving and attention to detail
• Interest or experience in scripting, data processing, workflow automation, testing, or technical documentation
• Familiarity with at least one relevant tool or language, such as Python, R, SQL, JavaScript, spreadsheets, notebooks, or low-code automation platforms
• Ability to translate requirements into clear technical steps
• Willingness to test work and document errors, assumptions, and limitations
• Awareness of data privacy, security, access, licensing, and responsible-AI considerations
• Ability to explain technical work to nontechnical collaborators
• Comfort asking questions when requirements or permissions are unclear
• Willingness to document methods, accept feedback, and revise substantive work
• Reliability when collaborating across disciplines and time zones

Advanced software-engineering experience is not required. Applicants should be able to demonstrate foundational technical reasoning and a willingness to learn within supervised assignments.

Prior experience relating to Kenya, Africa, infrastructure, international development, or public-sector data may be helpful but is not required.

APPLICATION MATERIALS

Applicants should submit through Handshake:

• A current résumé
• A cover letter explaining their interest in Project Guardian and describing relevant technical, data, automation, analytical, academic, or transferable experience
• One relevant work sample, such as a script, notebook, technical project, spreadsheet workflow, automation prototype, data-processing exercise, repository, technical report, or comparable academic, professional, volunteer, or independent work

Applicants should include a brief explanation of:

• The purpose of the work
• Their individual contribution
• The principal tools or languages used
• How the work was tested
• Any material limitations or dependencies

Applicants should remove or redact confidential, proprietary, personally identifiable, restricted, privileged, licensed, credential, or third-party information from submitted samples.

Applicants must not submit passwords, tokens, keys, private repositories without appropriate access authorization, or code they are not authorized to share.

A prior professional work sample is not required. Academic, volunteer, independent, simulated, synthetic-data, or appropriately redacted work is acceptable.

SELECTION PROCESS

Initial screening will be asynchronous. Applications will be assessed based on demonstrated alignment, technical reasoning, documentation quality, testing awareness, judgment, learning potential, and the relevance of submitted materials.

Finalists may be invited to complete a brief, time-limited educational evaluation exercise using hypothetical or synthetic information. The exercise may ask the applicant to outline an automation, review a short script, identify data-processing risks, propose validation tests, or explain how they would make a workflow reproducible.

Any selection exercise will be designed solely to evaluate the applicant’s reasoning and communication. It will not involve actual confidential project information, be used as uncompensated production work, or be incorporated into Project Guardian deliverables without separate written permission and appropriate arrangements.

Live conversations will be limited to finalists or circumstances requiring clarification. References may be requested selectively. Reasonable accommodations are available during the selection process.

Priority application deadline: October 2, 2026. The posting may close earlier or later based on application volume, institutional review timing, or program needs.

TECHNICAL, DATA, AND SECURITY LIMITATIONS

Unless separately authorized, the analyst may not:

• Access production, financial, personnel, authentication, or administrative systems
• Request, view, store, or use another person’s credentials
• Change permissions, security settings, domain settings, or production configurations
• Deploy code, databases, integrations, automations, software, or infrastructure
• Connect prototypes to production systems
• Scrape websites or services in violation of applicable terms, licenses, instructions, or technical controls
• Bypass authentication, rate limits, paywalls, robots directives, or access restrictions
• Conduct vulnerability scanning, penetration testing, exploit development, credential testing, or security monitoring
• Send automated external messages or submissions
• Initiate financial transactions, commitments, account changes, or access changes
• Collect unnecessary personal or sensitive information
• transfer project information to personal accounts, repositories, devices, or unapproved platforms
• Install unapproved software or dependencies in controlled environments
• Represent a prototype as secure, compliant, scalable, or production-ready

Technical assignments must use approved tools, appropriately authorized or synthetic data, bounded scope, documented testing, and supervised review.

CONFIDENTIALITY, ATTRIBUTION, LICENSING, AND RESPONSIBLE TOOL USE

Participants may encounter confidential, preliminary, proprietary, sensitive, licensed, or access-controlled information. Such information may be used only for authorized program purposes and handled according to applicable instructions.

Participants must:

• Protect confidential, personal, proprietary, licensed, and restricted information
• Use only authorized systems, accounts, storage locations, repositories, and transfer methods
• Access only materials reasonably necessary for assigned work
• Preserve source, definition, transformation, validation, test, review, and version records
• Follow applicable software, dataset, API, model, and content licenses
• Distinguish original work from quoted, adapted, collaborative, open-source, template-based, generated, or tool-assisted material
• Provide required attribution and preserve applicable license notices
• Disclose material use of generative artificial intelligence or automated tools when requested
• Independently verify generated code, citations, calculations, transformations, and technical recommendations
• Avoid fabricating tests, outputs, sources, approvals, performance claims, or findings
• Obtain approval before externally sharing project materials or including them in a portfolio
• Report suspected confidentiality, access, integrity, licensing, security, or information-handling problems promptly

Use of open-source code, external libraries, code-generation tools, or third-party services must be consistent with applicable licenses, approved purposes, confidentiality requirements, and security expectations.

PROFESSIONAL AND REGULATORY LIMITATIONS

This internship does not authorize the participant to provide or represent that they are providing software-engineering, cybersecurity, privacy, legal, regulatory, engineering, financial, compliance, or production-operations certification.

Scripts, notebooks, integrations, automations, and technical recommendations are educational and developmental materials unless expressly approved otherwise. They are not guarantees of security, accuracy, reliability, availability, scalability, compliance, or production suitability.

Questions requiring specialized professional judgment must be identified and routed for qualified review.

REPORTING AND AUTHORITY

The analyst will receive assignments and feedback from designated project or workstream leadership. Overall operational coordination remains with the Deputy Program and Operations Manager.

The analyst may:

• Complete approved internal scripting, transformation, validation, testing, prototyping, and documentation assignments
• Use specifically authorized tools, environments, and data
• Request clarification and authorized technical information
• Identify defects, risks, limitations, and questions requiring additional review
• Recommend bounded improvements or further testing
• Collaborate with assigned internal participants
• Revise work in response to documented feedback

The analyst may not independently:

• Obtain administrative or unrestricted access
• Deploy code or connect prototypes to production systems
• Change permissions, security controls, or controlled configurations
• Purchase or subscribe to tools or services
• Transfer, publish, disclose, or release project code or data
• Contact external stakeholders or service providers without approval
• Make legal, financial, employment, procurement, partnership, technical, or contractual commitments
• Represent the company, project, university, or any institution externally
• Approve final architecture, security, privacy, legal, or production-readiness conclusions
• Promise deployment, funding, partnership, academic credit, employment, publication, or any project outcome
• Authorize another participant to exercise any of these powers

Final decisions concerning access, architecture, security, privacy, deployment, procurement, data governance, external disclosure, and system adoption remain with authorized program leadership and, where necessary, qualified professionals.

All external outreach, data transfers, purchases, deployments, representations, commitments, and releases require prior authorization.

ELIGIBILITY AND INSTITUTIONAL CONDITIONS

Applicants must be able to participate remotely during the stated program period and communicate reliably in an asynchronous-first environment.

Students seeking academic credit must contact their institution and initiate any required approval, registration, learning-agreement, or internship-verification process. Institutional approval and academic credit are not guaranteed.

Where required, participation may be conditioned on completion of appropriate institutional documentation, confidentiality agreements, information-handling acknowledgments, intellectual-property terms, conflict disclosures, acceptable-use requirements, or other reasonable program documentation.

International students and other applicants are responsible for confirming with their institution or qualified adviser whether participation is consistent with applicable academic, immigration, authorization, funding, or institutional requirements. Morsby, Gorman, McCarthy LLC does not provide immigration, visa, tax, legal, or employment-authorization advice.

EQUAL CONSIDERATION AND ACCOMMODATIONS

Morsby, Gorman, McCarthy LLC intends to consider applicants based on role-related qualifications, demonstrated readiness, learning potential, and program needs. Reasonable accommodations may be requested for the application process or internship experience.

Applicants are encouraged to describe relevant academic, professional, volunteer, military, community, entrepreneurial, caregiving, independent, or lived experience when it demonstrates transferable skills applicable to the role.