Project Overview I am seeking a highly analytical data specialist to help structure and automate a dataset built from multiple sources of communication records and work activity logs. The objective is to transform raw information into a structured analytical dataset that reconstructs timelines, work assignments, and labor attribution across multiple related entities. This dataset will support a Department of Labor–related matter. This project focuses on forensic reconstruction of activity and work attribution, not simple spreadsheet organization. Source Data The dataset will be built from multiple information sources including: • text message transcript exports (CSV format) • email exports • task management system logs • timesheets / labor entries • property ownership data • supporting notes and documentation Project Objectives Build a structured dataset capable of analyzing: • timeline of communications and work assignments • individuals issuing work instructions • entities connected to each task or activity • time and labor attribution across entities • patterns of cross-entity work activity Automation Requirement A key objective is creating a repeatable ingestion pipeline so new information can be added without rebuilding the dataset. The ideal solution will include scripts or automated workflows (Python preferred) capable of: • parsing text and email transcripts • normalizing timestamps and participants • identifying or tagging entities referenced in communications • merging new datasets into a master timeline • generating structured output tables for analysis Ideally, new datasets can be dropped into a folder and automatically processed into the structured timeline dataset. Expected Deliverables • cleaned master dataset (CSV or Excel) • entity-relationship mapping linking people, entities, and properties • unified timeline dataset combining communications and work logs • work attribution table linking labor entries to entities • documentation explaining the ingestion and automation workflow Preferred Skills • Python (Pandas, data parsing, automation) • data engineering / ETL pipelines • data cleaning and normalization • entity resolution and relationship mapping • timeline reconstruction from communication datasets • investigative or litigation data analysis Bonus Experience • digital forensics • OSINT / investigative research • compliance or fraud analysis • communications network analysis Data Security & Confidentiality The data involved in this project is sensitive in nature and may contain information subject to HIPAA protections. Protecting confidentiality and maintaining secure handling of all materials is essential. Please describe your practices for protecting sensitive datasets, including: • how you store and handle confidential files • encryption or secure storage methods used during analysis • whether you work within secure local environments or protected cloud systems • any experience working with regulated data environments (HIPAA, legal datasets, financial records, etc.) The selected freelancer will be required to maintain strict confidentiality and may be asked to sign a non-disclosure agreement prior to receiving access to any project files. Application Request Please include the following in your proposal: A brief description of how you would approach building a structured timeline dataset from mixed sources such as texts, emails, and work logs. An example of a messy dataset you cleaned, automated, or structured for investigative or analytical use. A short overview of the tools or scripting languages you would likely use for this project.