Senior Director, Analytics & Data Science

Crusoe•San Francisco, CA

1d•$310,000 - $355,000

About The Position

As Senior Director of Analytics & Data Science, you will play a key role in how Crusoe makes decisions. This is a new role leading a newly formed team of centralized analysts and data scientists who are embedded directly with the business units and domains they support. Your mandate spans cloud, customer experience, construction, power, manufacturing, and more. The analytics team owns the business logic, certified data models, and reporting that leadership relies on, and partners closely with our Data team, which owns and maintains the data platform and the ingestion of data from source systems. We are seeking a hands-on leader who is energized by building something from the ground up. The ideal candidate is deeply technical, credible with executives and operators alike, and drawn to complex, cross-functional problems. Reporting into the CFO/COO organization, you will define the operating model, hire and develop the team, set the standards to maintain consistent metrics across the company, and be a key partner to leadership in making faster, better-informed decisions. In the near term, much of the work is foundational — building the data models and definitions that rigorous analysis depends on. As that foundation matures, the team will take on more forecasting, experimentation, and predictive work.

Requirements

Analytics & Data Science Leadership: 15+ years in analytics or data science, with 5+ years leading teams that include data scientists as well as analysts and analytics engineers. You’ve hired for both disciplines and set technical standards across them.
Technical Depth: Fluency with SQL and Python, strong data modeling instincts, and hands-on experience with dbt or equivalent. You can build a certified data mart yourself and review your team’s models substantively.
Data Science Depth: Direct experience applying forecasting, experimental design, and statistical inference to business problems, including ownership of models in production.
Modern Data Stack Experience: Experience with a modern data stack (BigQuery, dbt, Airflow, or equivalents).
Governance Judgment: Experience defining and certifying metrics, testing business rules, resolving entities across systems, and holding the line on definitions when a stakeholder wants their own version of a number.
Business and Financial Acumen: Comfort with P&L mechanics, unit economics, capital projects, and returns analysis. You understand why you are building something, not just what, and you can connect an operational metric to a financial outcome.
Executive Communication: Credibility in a room with the CFO and business unit leaders. You can defend a number, explain a method to a non-technical audience, and disagree with a stakeholder without damaging the relationship.
Organizational Navigation: Proven ability to operate in a dotted-line structure with competing priorities, earn trust from leaders within the business units, and optimize for the whole company rather than any one team.
Ownership: Track record of being the go-to person for "is this number right?" You find problems before they surface in an executive meeting and you own the resolution.
Comfort Building from Scratch: You are energized by unpaved paths, incomplete data, and processes that do not exist yet.

Nice To Haves

Advanced degree (Master's or Ph.D.) in a quantitative field (e.g., Data Science, Statistics, Computer Science) or equivalent experience.
Experience in capital-intensive or operationally complex industries: energy, construction, manufacturing, supply chain, or data center development.
Familiarity with cloud or infrastructure business models, including ARR, utilization, and capacity planning economics.
Hands-on use of AI-assisted coding tools such as Claude Code, Cursor, or GitHub Copilot to accelerate analytical work.
Experience applying forecasting or optimization to physical operations, e.g., capacity planning, supply chain, energy, or fleet management.
Experience in a hypergrowth company where processes were still being built.
Experience enabling LLM or MCP-based self-service analytics on top of governed data models.

Responsibilities

Stand Up the Central Team: Work with leaders across the business to understand their analytical needs, and design the staffing and operating model that meets them. Define how analysts are deployed, how work is prioritized, and how the team engages with each domain.
Build and Develop the Team: Recruit and grow analysts and data scientists who pair technical depth with business credibility. Build the career path and development model to retain talent.
Set Standards for Analytical Rigor: Define how the team approaches statistical and modeling work, including experimental design, forecasting, and how uncertainty is communicated to decision-makers. Review your team's methods substantively, and work hands-on where needed.
Build the Data Science Capability: Determine where predictive and statistical methods will have the most impact and sequence the investment accordingly. Near-term opportunities span power utilization, capacity and demand forecasting, schedule and cost forecasting, fleet reliability, and product experimentation.
Own Certified Marts and Metric Definitions: Own the business logic, certified data marts, metric dictionary, and business-rule testing that keep metrics consistent across the company. Establish peer review as a standard practice so work is checked by qualified colleagues before it reaches business leaders.
Own Executive and Cross-Business Reporting: Own certified reporting for the executive team, the board, and questions that span business units, with operational reporting in the business units built on the marts your team certifies.
Drive High-Stakes Decision Support: Turn the company's most complex, cross-functional questions into clear analysis and recommendations that leadership can act on.
Build Toward Self-Service: Create the conditions for the business to answer routine questions without routing through an analyst, including the table and field descriptions our data MCP reasons over, on top of certified marts with properly defined metrics.
Maintain Close Proximity to the Business: Keep analysts embedded in their domains through staff meetings, planning cycles, and shared goals, and align central and business unit priorities so analysts always know what matters most.
Partner with the Data Team: Collaborate with the Data team on the data foundation, contributing the domain context and business definitions that help new source systems and data models land well for the business.