Director Data Science & AI Finance

This Director-level role at Dropbox sits at a compelling intersection of data science, data engineering, and applied AI for Finance — a rare combo that commands significant market premium. Director of Data Science roles at tech companies of Dropbox's scale typically benchmark at $220K–$300K total cash, so the posted range of $203K–$309K is competitive to slightly above-market
, especially for Zone 3 candidates.

• Building 0-to-1 AI/ML capabilities for Finance is a highly transferable leadership narrative for future VP/C-suite moves.
• Cross-disciplinary ownership (DS + DE) is increasingly rare and valued — most Director roles are siloed.
• Generative AI integration into FP&A workflows is an emerging, high-demand specialty area.

Green flags: Dropbox's virtual-first model signals genuine remote culture, not performative flexibility. The emphasis on "durable skills" and growth mindset suggests psychological safety. Managing only 2 direct reports at Director level means less HR overhead and more strategic focus.

Yellow flags to probe: A team of 2 supporting an entire Finance org can signal under-resourcing — ask about headcount growth plans in interviews. The "reactive to proactive" transformation language suggests the team may currently be overwhelmed by ad-hoc requests.

Ideal next roles after this: VP of Data (Finance or Enterprise), Chief Data Officer at a mid-size company, or VP of Analytics at a growth-stage startup. The Finance domain expertise + AI leadership combo is a strong differentiator.

This is a critical leadership role at the intersection of data science, data engineering, finance, and AI. As Director, Data Science & Data Engineering for Finance, you will lead a small, high-impact team that enables Finance to make better decisions through trusted data, robust analytical systems, applied modeling, and increasingly AI-powered capabilities.

You will manage two direct reports: a data scientist and a data engineer. Together, the team supports the financial organization across recurring business operations, driver-model maintenance, knowledge coordination, analytical insights, and the development and operationalization of AI and machine-learning use cases. The role requires enough technical depth to guide both disciplines, while maintaining a clear focus on business outcomes, reliability, and team growth.

You will partner closely with Finance leadership, Engineering, Data, Product, and other cross-functional stakeholders to establish priorities, improve the quality and accessibility of financial data, and shape a forward-looking roadmap. You will be expected to move the team beyond request-driven execution: identifying the highest-leverage opportunities, clarifying the problems worth solving, and creating the operating mechanisms needed to deliver durable impact.

This is an opportunity to shape how Finance uses data and AI at a time when the underlying technologies, operating models, and expectations are changing quickly. The successful candidate will bring a growth mindset, a strong background in either data science or data engineering, and the ability to lead effectively across both disciplines.

• Manage and grow a multidisciplinary team: Lead, coach, and develop a data scientist and a data engineer, creating clear expectations, meaningful development plans, effective delegation, and a strong culture of ownership and continuous improvement.
• Set the team's strategy and priorities: Translate Finance and company goals into a focused portfolio of work. Prioritize ruthlessly across strategic initiatives, recurring operational responsibilities, model maintenance, knowledge coordination, and technical debt.
• Pr…