Job Description
JOB DESCRIPTION:
4–7 years in data/document analytics with exposure to legal or contract text formats (e.g., redlines, tracked changes, clause blocks). Deep understanding of legal meaning is not required; comfort working with structured legal language as a text dataset is essential. This role extracts and synthesizes clause-level redline patterns from Marmon templates and historical negotiations. Deliverables center on structured intelligence—variant clusters, exception frequency, edit rationales, and drift over time—to inform contract SMEs and future playbook authors. The role does not draft fallback language or determine legal positions; it provides the evidence and pattern clarity required for those decisions.
ESSENTIAL FUNCTIONS:
- Redline Pattern Extraction & Variant Discovery
- Analyze historical tracked changes and markups at scale to identify recurring edit patterns, counterparty tendencies, and structured clause-variation clusters in legal text.
- Group similar edits into normalized variant sets without determining fallback positions or legal acceptability. Clause Change Clustering & Exception Mapping
- Classify edits by frequency, type (add / delete / narrow / broaden), and location in the document.
- Surface anomalies and outliers for legal SME review, not interpretation. LLM-Assisted Summarization & Tagging
- Use AI tools to accelerate classification of edit intent and theme categorization while maintaining analyst oversight and QC.
EDUCATION AND EXPERIENCE: • Bachelor’s degree (B.Tech / B.E / B.Sc / B.Com / BA or LL.B). • Post-grad in Analytics, Data, Legal Ops optional, not required. • 4–7 years in data/document analytics; exposure to legal text formats helpful. • No drafting, negotiation, or fallback expertise required. • Experience with LLM-assisted classification or Python-based text parsing preferred (operate, not architect). • Experience working with global teams across time zones preferred.
4–7 years in data/document analytics with exposure to legal or contract text formats (e.g., redlines, tracked changes, clause blocks). Deep understanding of legal meaning is not required; comfort working with structured legal language as a text dataset is essential. This role extracts and synthesizes clause-level redline patterns from Marmon templates and historical negotiations. Deliverables center on structured intelligence—variant clusters, exception frequency, edit rationales, and drift over time—to inform contract SMEs and future playbook authors. The role does not draft fallback language or determine legal positions; it provides the evidence and pattern clarity required for those decisions.
ESSENTIAL FUNCTIONS:
- Redline Pattern Extraction & Variant Discovery
- Analyze historical tracked changes and markups at scale to identify recurring edit patterns, counterparty tendencies, and structured clause-variation clusters in legal text.
- Group similar edits into normalized variant sets without determining fallback positions or legal acceptability. Clause Change Clustering & Exception Mapping
- Classify edits by frequency, type (add / delete / narrow / broaden), and location in the document.
- Surface anomalies and outliers for legal SME review, not interpretation. LLM-Assisted Summarization & Tagging
- Use AI tools to accelerate classification of edit intent and theme categorization while maintaining analyst oversight and QC.
EDUCATION AND EXPERIENCE: • Bachelor’s degree (B.Tech / B.E / B.Sc / B.Com / BA or LL.B). • Post-grad in Analytics, Data, Legal Ops optional, not required. • 4–7 years in data/document analytics; exposure to legal text formats helpful. • No drafting, negotiation, or fallback expertise required. • Experience with LLM-assisted classification or Python-based text parsing preferred (operate, not architect). • Experience working with global teams across time zones preferred.
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