Data Scientist (Client Insights & Distribution Analytics)

Position Summary:

Grayscale is hiring a Data Scientist to strengthen and scale the analytical foundation supporting our distribution organization. This role sits at the intersection of data engineering, advanced analytics, and commercial strategy. The role will own the ingestion, analysis, and reconciliation of large, complex, and disparate commercial datasets and develop analytical frameworks that directly inform territory design, account prioritization, and revenue strategy.

This is a hands-on, technically rigorous role requiring strong SQL and Python fluency, sound data modeling instincts, and the ability to translate analysis into actionable business decisions.

Responsibilities:

• Ingest and harmonize third-party intermediary datasets (e.g., DTCC, 13F, wirehouse and broker-dealer datapacks) to create advisor- and platform-level views of holdings, flows, and wallet share.
• Design and maintain entity resolution frameworks to reconcile advisors, households, platforms, and institutions across fragmented third-party datasets. Build scoring systems and opportunity sizing frameworks that quantify advisor-level revenue potential and optimize coverage allocation.
• Write production-grade SQL and Python to power recurring analytics and decision-making workflows.
• Conduct predictive and diagnostic analyses to identify drivers of engagement, conversion, and net flows.
• Partner with distribution leadership to quantify commercial opportunities and evaluate performance drivers.
• Build clear, decision-ready dashboards and analytical outputs in Tableau that support senior leadership.
• Identify structural data quality issues and implement durable fixes that improve reliability and reduce operational risk.
• Build scoring systems and opportunity sizing frameworks that quantify advisor-level revenue potential and optimize coverage allocation.
• Contribute to pipeline resilience and workflow standardization to reduce key-person dependency.

Prior Experience/Requirements:

• 7–12 years of experience in data science, advanced analytics, or analytics engineering within asset management, wealth management, fintech, or financial services.
• Advanced SQL proficiency (complex joins, window functions, performance optimization) with demonstrated experience working across large datasets.
• Demonstrated ability to translate complex data into actionable commercial insights.
• Experience supporting sales strategy, territory design, account segmentation, revenue analytics, or client insights functions preferred.
• Strong documentation habits and disciplined approach to reproducibility and process design.
• Ability to operate independently in a scaling environment.
• Knowledge of ETF structures and intermediary distribution channels preferred.