Director, Data Science & Engineering, Finance

Dropbox

4d•$203,000 - $309,000

About The Position

This is a leadership role at the intersection of data science, data engineering, finance, and AI. The Director, Data Science & Engineering for Finance leads a small, high-impact team that enables Finance to make better decisions through trusted data, robust analytical systems, applied modeling, and AI-powered capabilities. The role manages two direct reports—a data scientist and a data engineer—and supports Finance across recurring business operations, driver-model maintenance, knowledge coordination, analytical insights, and the development and operationalization of AI and machine-learning use cases. It requires technical depth across both disciplines, business-outcome focus, and attention to reliability and team growth. The Director partners with Finance leadership, Engineering, Data, Product, and other stakeholders to set priorities, improve the quality and accessibility of financial data, and shape a forward-looking roadmap. The role is expected to move the team beyond request-driven execution by identifying high-leverage opportunities, clarifying problems to solve, and establishing operating mechanisms for durable impact. The successful candidate brings a growth mindset, a strong foundation in either data science or data engineering, and the ability to lead across both disciplines.

Requirements

Bachelor’s degree or above in Computer Science, Data Science, Statistics, Mathematics, Engineering, Economics, or a related quantitative discipline, or equivalent practical experience.
Significant experience applying data science, machine learning, analytics, data engineering, or a combination of these disciplines to business problems.
Experience directly managing and developing technical professionals, ideally including data scientists and data engineers or closely adjacent roles.
A strong professional foundation in either data science or data engineering, with demonstrated ability to work effectively across the other discipline.
Experience leading a portfolio of projects from problem definition through delivery, adoption, measurement, and ongoing operation.
Experience with data quality, data pipelines, analytical datasets, model development or maintenance, experimentation or evaluation, and production or operational support.
Demonstrated ability to use data and technology to influence business decisions and deliver measurable impact.
Strong communication and stakeholder-management skills, including explaining technical concepts, uncertainty, and trade-offs to Finance and executive audiences.
A growth mindset: actively seeks feedback, learns new technologies and domains, adapts to changing conditions, and fosters the same in the team.
Ability to operate with a high degree of autonomy and ownership in an ambiguous, fast-changing environment.

Nice To Haves

Experience supporting Finance, financial planning and analysis, forecasting, corporate strategy, accounting, or other business functions with high standards for accuracy and control.
Experience building or scaling AI/ML capabilities, including generative AI, predictive modeling, model evaluation, workflow automation, or AI-assisted decision support.
Experience managing a hybrid or multidisciplinary team spanning data science, analytics, data engineering, or software engineering.
Experience building 0-to-1 capabilities, establishing new operating models, or transforming a team from reactive execution to proactive partnership.
Experience with data governance, observability, model risk management, responsible AI, or other practices supporting reliable and auditable business decisions.
Experience working in a technology company or other complex, data-rich environment.

Responsibilities

Lead, coach, and develop a data scientist and a data engineer, setting clear expectations, development plans, effective delegation, and a culture of ownership and continuous improvement.
Translate Finance and company goals into a focused portfolio of work and prioritize strategic initiatives, recurring operations, model maintenance, knowledge coordination, and technical debt.
Provide technical leadership across data science and data engineering, guiding problem framing, analytical and modeling approaches, data architecture, pipeline quality, testing, monitoring, documentation, and production readiness.
Identify high-value opportunities for AI, machine learning, automation, and advanced analytics in Finance workflows, and establish standards for experimentation, evaluation, interpretability, human oversight, privacy, and monitoring.
Partner with data engineering and Finance stakeholders to improve source-of-truth datasets, data quality, lineage, observability, documentation, and reliability.
Ensure models and analyses supporting financial planning, forecasting, business-driver understanding, and decision-making are developed, maintained, evaluated, and improved, and connect technical work to measurable business outcomes.
Establish lightweight processes for intake, prioritization, roadmap planning, model and pipeline reviews, incident response, documentation, knowledge sharing, and stakeholder communication.
Communicate data, modeling, and engineering considerations, implications, recommendations, risks, and trade-offs clearly to technical and non-technical audiences, including senior stakeholders.
Partner with Finance leaders, analysts, technical teams, product partners, and executives; seek diverse perspectives, lead productive debate, and align stakeholders when priorities or approaches differ.
Identify ways to eliminate low-value work, automate recurring tasks, improve service levels, and increase the team's strategic contribution; make the case for and lead changes to the operating model when needed.