Data Scientist

Kforce has a client that is seeking Data Scientist in Plantation, FL. Overview: The client is looking for data analyst that will transcribe data in mermaid charts to be contextual for AI usage. This role is responsible for translating complex, domain-specific business language and visual system artifacts into standardized, machine-readable data representations that can be consumed by analytics platforms, knowledge bases, and large language models (LLMs). The individual will work closely with business stakeholders, data teams, and engineering partners to transcribe charts, diagrams, and system documentation (including data dictionaries and architectural models) into structured semantic formats using text-based diagramming and modeling tools (e.g., Mermaid or similar). The position ensures that niche, organization-specific terminology (such as brand, vessel, operational, and industry-specific language) is normalized and mapped to canonical data definitions, enabling consistent interpretation across systems, teams, and AI-driven tools. This role plays a critical function in enabling enterprise search, AI research, and advanced analytics by creating a reliable semantic layer that bridges human language and technical systems.

• Data Modeling & Data Dictionaries - Ability to define, maintain, and normalize canonical data definitions
• Semantic Modeling & Metadata Management - Translating domain-specific language into machine-readable structures
• Text-Based Diagramming Tools - Experience with Mermaid, PlantUML, UML, or similar diagram-as-code frameworks
• Domain Language Translation - Converting business, operational, and industry-specific terminology into standardized technical representations
• AI/LLM Data Enablement - Preparing structured data and documentation for enterprise search, AI, or LLM consumption
• SQL & Structured Data Concepts - Understanding schemas, relationships, and data integrity
• Version-Controlled Documentation - Git-based workflows for maintaining diagrams and technical artifacts
• Systems & Architecture Documentation - Capturing system flows, entity relationships, and dependencies
• Cross-Functional Communication - Working with business, IT, data, and engineering teams
• Requirements Elicitation - Extracting implicit knowledge and tribal language from subject matter experts
• Data Governance Awareness - Standards, naming conventions, and semantic consistency across platforms