Director, Data Science & Engineering, Finance
Dropbox · Remote - US: Select locations
About this role
**Role Description** 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. --- **Responsibilities** - **Manage and grow a multidisciplinary team:** Lead, coach, and develop a data scientist and a data engineer; create 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 across strategic initiatives, recurring operational responsibilities, model maintenance, knowledge coordination, and technical debt. - **Provide technical leadership across data science and data engineering:** Guide problem framing, analytical and modeling approaches, data architecture, pipeline quality, testing, monitoring, documentation, and production readiness. Ask the right questions, recognize trade-offs, and raise the technical bar. - **Advance AI and modeling capabilities:** Identify high-value opportunities to apply AI, machine learning, automation, and advanced analytics to Finance workflows. Establish standards for experimentation, evaluation, interpretability, human oversight, privacy, and ongoing monitoring. - **Build trusted data foundations:** Improve source-of-truth datasets, data quality, lineage, observability, documentation, and reliability—ensuring recurring reporting, driver models, and downstream analyses are based on accurate and well-governed data. - **Own analytical and modeling outcomes:** Ensure models and analyses support financial planning, forecasting, business-driver understanding, and decision-making—connecting technical work to measurable business outcomes. - **Create scalable operating mechanisms:** Establish lightweight processes for intake, prioritization, roadmap planning, model and pipeline reviews, incident response, documentation, knowledge sha
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